text:
Three riverboat gamblers, Bruce, George and Ben, are discussing tomorrow's sharpshooting contest between Annie Oakley and Lillian Smith. Today's Deadwood Pioneer newspaper contains reports of their previous contests in Table 1 and of their contests with Frank Butler in Table 2.
|Table 1. Previous Contests between Annie and Lillian.
(1 = Winner)
|Table 2. Previous Contests between Annie or Lillian and Frank.
(1 = Winner)
|Shooter's Score
|Shooter's Score
"Tomorrow it's Annie against Lillian. Here's how to get the odds correct." says Bruce. "Let's use PMLE1. In each Table, Annie and Lillian were in the same situation three times, once for each row. Annie won twice. Lillian won once. The odds in both Tables are 2/1. Annie and Lillian are ln(2/1) = 0.69 logits apart."
"We get those same odds of 2/1 from both Tables using CMLE2,3." agrees George.
"JMLE4 and MMLE5 estimate that the odds for both Tables are 4/1" says Ben. "To produce the correct odds of 2/1 for the direct pairwise comparison of Annie and Lillian in Table 1, we must adjust for JMLE estimation bias6. However, the odds for the indirect pairwise comparison of Annie and Lillian in Table 2 are 4/1. JMLE/MMLE are correct. There is no JMLE estimation bias for Table 2."
"No! No!" objects George. "JMLE estimates are always biased7, even though the bias reduces quickly for larger datasets8. Ben, you are way off target!"
|Table 3. Previous Contests between Annie or Lillian and Frank (redrawn).
(1 = Winner)
|Shooter's Score
|2 of 3
|3 of 6
|1 of 3
"It's you guys who can't shoot straight" says Ben. "Let's redraw Table 2 so that each row is a separate contest of sharpshooters. Here it is in Table 3 where all the participants are columns and there is one row for each pairwise contest, exactly like Table 1. Let's take Bruce's PMLE logic for Table 1 and apply it to Table 3. All the row scores are 1. In the upper three contests, Annie scores 2 and Frank scores 1. The odds are 2/1 for Annie against Frank. In the lower three contests, Frank scores 2 and Lillian scores 1. The odds are 2/1 for Frank against Lillian. Combining these, the odds for Annie against Lillian are (2/1) * (2/1) = 4/1. They are ln(4/1) = 1.39 logits apart, exactly as JMLE tell us!"
"That is exciting!" exclaims Bruce. "We can extend Table 3 to much larger competitive situations such as Basketball9 and Tennis using PMLE or bias-adjusted JMLE10."
"Ben, why didn't you explain this to me years ago?" says George. "In Tables 1 and 3, the paired comparisons are direct. In Table 2 the comparisons are indirect. Table 2 is an abbreviated version of Table 3. When we treat the comparisons in Table 2 as direct, we distort the meaning of the data, resulting in biased estimates."
"Exactly!" says Ben, "When a dataset is directly pairwise, as in Tables 1 and 3, CMLE/PMLE estimates are accurate and unbiased. We must bias-adjust JMLE estimates. For a dataset of indirect comparisons like Table 2, JMLE estimates are unbiased. CMLE/PMLE estimates for any dataset are biased if reformatting that dataset to be directly pairwise produces different CMLE/PMLE estimates."
Dear Reader: Would you like more evidence? raschdat1.rda is a dichotomous dataset of 30 items and 100 persons distributed in the eRm package. In www.rasch.org/rmt/a/shootout.zip, there are conventional Table 2 (30x100) versions of raschdat1 for CMLE, JMLE, MMLE, and PMLE, also Table 3 pairwise (130x3000) versions for JMLE and PMLE, together with their estimates (see Figure 1) and the Excel worksheet of the Figures. In Figure 2, Table 3 curves track with Table 2 JMLE curves, confirming Ben's claim that JMLE is unbiased for conventional datasets.
Figure 1. MLE estimates for raschdat1
Figure 2. MLE estimates for raschdat1 (Excerpt).
John Michael Linacre
Gregory Chan (RUMM2020 Analyses)
Raymond J. Adams (ConQuest analyses)
1. PMLE, Pairwise Maximum Likelihood Estimation, in RUMM2030 and Facets.
2. CMLE, Conditional Maximum Likelihood Estimation, in eRm.
3. CMLE for Tables 1 and 2: All the rows are scored 1 on the 2 items, so we only need the probabilities for a row score of 1. Let's call P(10) the probability that Annie wins and Lillian loses, then P(01) is the opposite. For each row the total probability for a score of 1 is P(10) + P(01). In each Table, Annie scored 2 in 3 attempts, so 2 = 3 * P(10) / (P(10) + P(01)). Lillian scored 1 in 3 attempts, so 1 = 3 * P(01) / (P(10) + P(01)). Now, divide those two equations, then the odds are P(10)/P(01) = 2/1. ln(2/1) = 0.69 logits.
4. JMLE, Joint Maximum Likelihood Estimation, in ConQuest, Facets and Winsteps. The JMLE estimates are the ones for which the observed marginal score equals the expected marginal score for each row and column.
5. MMLE, Marginal Maximum Likelihood Estimation, in ConQuest. MMLE estimates are the ones for which the observed marginal score for each column equals the expected marginal score, and the row parameters are modeled to have a normal distribution.
6. Using Winsteps, JMLE pairwise estimation bias is adjusted by Paired=Yes
7. Andersen E.B. (1970) Asymptotic properties of conditional maximum likelihood estimators. Journal of the Royal Statistical Society B 32, 283–301
8. Wright, B.D. (1988) The efficacy of unconditional maximum likelihood bias correction: Comment on Jansen, Van den Wollenberg, and Wierda. Applied Psychological Measurement, 12, 315-318. www.rasch.org/memo45.htm
9. Linacre J.M. (2001) Paired comparisons for measuring team performance. RMT, 15:1, 812. www.rasch.org/rmt/rmt151w.htm
10. Linacre J.M. (1997) Paired comparisons with standard Rasch software. RMT, 11:3, 584-5. www.rasch.org/rmt/rmt113o.htm
Available Rasch software is listed at www.rasch.org/software.htm.
Linacre J.M., Chan G., Adams R.J. (2013) An "Estimation Bias" Shootout in the Wild West: CMLE, JMLE, MMLE, PMLE. Rasch Measurement Transactions, 27:1 p.1403-5
Please help with Standard Dataset 4: Andrich Rating Scale Model
|Rasch Publications
|Rasch Measurement Transactions (free, online)
|Rasch Measurement research papers (free, online)
|Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch
|Applying the Rasch Model 3rd. Ed., Bond & Fox
|Best Test Design, Wright & Stone
|Rating Scale Analysis, Wright & Masters
|Introduction to Rasch Measurement, E. Smith & R. Smith
|Introduction to Many-Facet Rasch Measurement, Thomas Eckes
|Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, George Engelhard, Jr.
|Statistical Analyses for Language Testers, Rita Green
|Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar
|Journal of Applied Measurement
|Rasch models for measurement, David Andrich
|Constructing Measures, Mark Wilson
|Rasch Analysis in the Human Sciences, Boone, Stave, Yale
|in Spanish:
|Análisis de Rasch para todos, Agustín Tristán
|Mediciones, Posicionamientos y Diagnósticos Competitivos, Juan Ramón Oreja Rodríguez
|Rasch Measurement Forum to discuss any Rasch-related topic
Go to Top of Page
Go to index of all Rasch Measurement Transactions
AERA members: Join the Rasch Measurement SIG and receive the printed version of RMT
Some back issues of RMT are available as bound volumes
Subscribe to Journal of Applied Measurement
Go to Institute for Objective Measurement Home Page. The Rasch Measurement SIG (AERA) thanks the Institute for Objective Measurement for inviting the publication of Rasch Measurement Transactions on the Institute's website, www.rasch.org.
|Coming Rasch-related Events
|July 31 - Aug. 3, 2017, Mon.-Thurs.
|Joint IMEKO TC1-TC7-TC13 Symposium 2017: Measurement Science challenges in Natural and Social Sciences, Rio de Janeiro, Brazil, imeko-tc7-rio.org.br
|Aug. 7-9, 2017, Mon-Wed.
|In-person workshop and research coloquium: Effect size of family and school indexes in writing competence using TERCE data (C. Pardo, A. Atorressi, Winsteps), Bariloche Argentina. Carlos Pardo, Universidad Catòlica de Colombia
|Aug. 7-9, 2017, Mon-Wed.
|PROMS 2017: Pacific Rim Objective Measurement Symposium, Sabah, Borneo, Malaysia, proms.promsociety.org/2017/
|Aug. 10, 2017, Thurs.
|In-person Winsteps Training Workshop (M. Linacre, Winsteps), Sydney, Australia. www.winsteps.com/sydneyws.htm
|Aug. 11 - Sept. 8, 2017, Fri.-Fri.
|On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com
|Aug. 18-21, 2017, Fri.-Mon.
|IACAT 2017: International Association for Computerized Adaptive Testing, Niigata, Japan, iacat.org
|Sept. 15-16, 2017, Fri.-Sat.
|IOMC 2017: International Outcome Measurement Conference, Chicago, jampress.org/iomc2017.htm
|Oct. 13 - Nov. 10, 2017, Fri.-Fri.
|On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
|Oct. 25-27, 2017, Wed.-Fri.
|In-person workshop: Applying the Rasch Model hands-on introductory workshop, Melbourne, Australia (T. Bond, B&FSteps), Announcement
|Jan. 5 - Feb. 2, 2018, Fri.-Fri.
|On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
|Jan. 10-16, 2018, Wed.-Tues.
|In-person workshop: Advanced Course in Rasch Measurement Theory and the application of RUMM2030, Perth, Australia (D. Andrich), Announcement
|Jan. 17-19, 2018, Wed.-Fri.
|Rasch Conference: Seventh International Conference on Probabilistic Models for Measurement, Matilda Bay Club, Perth, Australia, Website
|April 13-17, 2018, Fri.-Tues.
|AERA, New York, NY, www.aera.net
|May 25 - June 22, 2018, Fri.-Fri.
|On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
|June 29 - July 27, 2018, Fri.-Fri.
|On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps), www.statistics.com
|Aug. 10 - Sept. 7, 2018, Fri.-Fri.
|On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com
|Oct. 12 - Nov. 9, 2018, Fri.-Fri.
|On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
|The HTML to add "Coming Rasch-related Events" to your webpage is:
<script type="text/javascript" src="http://www.rasch.org/events.txt"></script>
The URL of this page is www.rasch.org/rmt/rmt271a.htm
Website: www.rasch.org/rmt/contents.htm


text:
Let us know when your receive great advice so we can thank our lawyers and point others with similar questions in the right direction.Got it!
i am 17 yrs old. i have been living in america for 15 yrs. i really want to get my papers so i can work, drive, and go to college. this is a very stressful situation on my part. can someone pease help me.
Before you do anything, please consult with an experienced immigration attorney in your area because if you do not have current status to be in the U.S., any contact with the federal government may subject you to removal proceedings in you do not have valid immigration status.
You state that you are "an immigrant," but are you a Lawful Permanent Resident, or a do you have derivative status through your parents work visas? Were you admitted or did you enter the U.S. without inspection? Did any family member sponsor or petition for your immigrant visa?
The answers to these questions make a huge difference in your situation.
Good luck.
Stanley Dale Radtke, Esq.
220 Montgomery Street, Suite 416
San Francisco, CA 94014
Post a free question on our public forum.Ask a Question
- or -
Search for lawyers by reviews and ratings.Find a Lawyer

subset: commoncrawl


text:
Your Store:
item(s) totaling $
See if this part fits a specific vehicle.
|This product doesnt fit any of your saved vehicles
|Check Fit for another vehicle
|Cable Material:
|73 in
|Package Contents:
|Hood Release Cable
Do you have questions about this product?
Ask a question, get answers from real customers and in-house experts. For further assistance, please review your support options on our Contact Us page.
Q:How do you install this item and also release the hood with a broken hood release cable? Thank you sincerely-Jen
1 answer
A:For assisting you further on the Dorman - OE Solutions Hood Release Cable - part# 912-052, please provide the year, make, model and engine size of the vehicle. For any further assistance, please feel free to call us at: 1-877-238-2623 or visit the nearest Advance Auto Parts store and one of our Parts Pros will gladly assist you with any questions or concerns you may have.


text:
Free Gift Packaging
- Signature blue gift box
- Stylish silver ribbon
- Personal gift message
022 Gauge Heart-Shaped Nose Stud with Cubic Zirconia in 14K Rose Gold
Item# 18568584
A shimmering cubic zirconia accent inside of a heart-shaped frame sparkles in this 022 gage 14K rose gold nose ring.
Special Offers
Typographic errors are subject to correction. Merchandise enlarged to show detail and may not always be exactly as pictured. Promotions, prices, styles and availability may vary by store and online.


text:
About the Book
Maya visits her grandparents in Alaska and ends up searching for a missing river otter.
This magical adventure was a Geoduck Kid's Book Award Finalist and the winner of the SOS Children's Lit Award.
Features & Details
- Category Children
Standard Portrait, 7.75×9.75 in, 20×25 cm
26 Pages
- Publish Date Mar 26, 2010
See More
About the Creator
Ted and Dale, the authors/photographers still spend each winter in Alaska as caretakers on a floating fishing lodge. When not enjoying the beauty of the seals, otters, and other wildlife, they fill their days with drying, stacking, and restacking firewood for their woodstove. Life is good!
Log in or create an account to post a comment.
0 Comments
Delete this comment?
See More


text:
|Burren Black
|Burren Red
|Burren Gold
Rotin (37) Neratovice, Czech Republic | October 28, 2016
Visited 15 jul 2016
Really stylish Irish pub. On tap three their own beers and Guiness. Friendly staff.
Fine stop during travelling Ireland.
Ascelja (55) Neratovice, Czech Republic | July 25, 2016
Living pub on the mainroad through the village with nice old interior. 3 taps with their own beers, 1 cider, few commercial. This place has pleasant homely atmosphere. Nice beer stop if you´re traveling across Burren.
Sokolov (899) Moscow, Russia | March 24, 2015
Yet another excellent Irish place! Lager, ale & stout. All being of perfect taste except of ale which still needs a bit of tweaking. Add in a local top smoke house next door & get a perfect pairing for your drink!
Dogbrick (858) Columbus, Ohio | December 31, 2012
(Visited 11/2012). We stopped at the appropriately named Roadside Tavern on a day trip from Galway. There is a small private parking lot next to the building.
The interior is small with table seating on either side of the entrance and a small bar in the back. Travel kitsch adorns the walls and the place has a well-worn look. There is an adjacent room with more table seating.
This brewpub has 3 house beers on tap under the Burren label, which included a Gold Lager, Blonde Ale and Black Stout on our visit. All of the beers were decent. They had some bottled/canned stuff too.
Service by the owner/brewer was great. When we expressed interest in craft beer he became immediately enamored with us, which led me to believe the locals don't appreciate the locally brewed beer much. He offered to take us on a tour of his small facility, talked brewing in Ireland and the USA, and posed for pictures. Evidently they are also associated with a JRR Tolkein society of some kind as well. It was a great impromptu experience all around.
We did not have any food on our visit.
All in all this is a good beer spot to hit in County Clare.
Theydon_Bois (1034) Hazlemere, the lower Xhitlerns, England | August 5, 2012
Brewery tap for the Burren Brewery where all 3 of their beers, Black, Red and Gold are available on tap (along with a few commercial brews). This is the only place (as of Jan '12) you can get theire beers, they don't bottle either! Typical single room Irish pub with an extenstion via a small stair case that I think was the restaurant. We visited at 2pm on a Saturday and there were only 2 or 3 others in there. Service was prompt and friendly (not too difficult mind!). Lowish ratings as only 3 beers of note - however they were all good beers!


text:
$16.00 donated in past month
IMC Network
From the Open-Publishing Calendar
From the Open-Publishing Newswire
Indybay FeatureRelated Categories: U.S. | Police State and Prisons
Herman Wallace Update: Judge Denies State Appeal, Issues Second Order for Herman's Release
This release is a followup to our announcement this morning that Herman Wallace of the Angola 3 conviction has been overturned.
After this morning's bombshell ruling by Judge Jackson overturning Herman's conviction and ordering his immediate release, his legal team has spent the day trying to convince Louisiana officials to do as the Court requires and let him go free.
As some outlets have reported, an ambulance has been outside the prison all afternoon ready to transport him out of prison to supporter sponsored accommodations complete with hospice care, but the State stubbornly refused release and filed a motion to Stay.
Just moments ago, Judge Jackson issued an order denying the State's desperate attempt at keeping Herman behind bars, again ordered his release, and cautioned Louisiana officials that their failure to release him immediately would put them in Contempt of Court. We will update you as soon as we hear any more news.
Download PDF (320.4kb)


text:
There are are two reasons to restore a company:
If a company is to carry on trading the file must be brought up to date with all missing Annual Returns and Accounts.
If the Company is only being restored to release assets, filing of Annual Returns and Accounts will not be required, but undertakings by the Applicant or Directors of the company will have to be given to the Court agreeing to re-apply for striking off once the assets have been released.
Once a company is dissolved, its bank account will be frozen and its assets are deemed to be Bona Vacantia and will belong to the Crown.
The Registrar of Companies will ask for the the Annual Returns and Accounts to be filed prior to restoration to bring the public file of the company up to date.
In order for an application to the High Court to be made to restore to the register a company which has been struck off and dissolved, it will be necessary for an application to be made either by a shareholder or director of the company.
If the Treasury Solicitor and the Registrar of Companies then consent, this needs to be followed up with an Affidavit of Service exhibiting the Bona Vacantia letter and provided that everything is done in accordance with the Treasury Solicitor's requirements and an agreement is given to pay his costs, the matter proceeds to a hearing at which representation is required before the Order is made.
We will:
Company restoration by Administrative Restoration and Company Restoration by Court Order.
Administrative Restoration Our fee to deal with the company restoration for you is £450 + disbursements of:
Once the application for restoring your company has been made we will continue to monitor and advise you of the progress and provide you with confirmation when your company has been restored.
Company Restoration by Court Order - Our fee to deal with the company restoration for you is £600 + disbursements of:
The restoration needs to be made in the name of the Company and joined by a shareholder or director.
It is not necessary for an applicant to attend court as we will appear on your behalf
We can take care of all the formalities associated with the restoration of your UK Limited Company.
In order to restore a company to the Register, we make an application by way of claim form in the High Court and this has to be supported by the affidavit of a shareholder or director.
We can download or prepare the documents if you do not have the originals and prepare and file any outstanding Annual Returns and we assist with the filing of the Report and Accounts if necessary.
A company may be restored by a court order by a shareholder or director of the company, within 20 years of dissolution.
The company or any person who was a member or creditor of the company when it was dissolved can make the application.
For companies struck off at the company's own request any of the parties who must be notified of the application can apply to the Court within 20 years of the date of dissolution for the name of the company to be restored to the Register. The Court may order restoration if it is satisfied that:
Companies struck off at the instigation of the Registrar - the company, or a member or creditor of it can apply to the Court for restoration within 20 years of the date of dissolution. When a company applies for its own restoration, a member of the company must also be joined in the proceedings so as to be responsible for the costs of the Registrar of Companies.
To find out which court has jurisdiction to deal with the application we need to check the Registered Office of the company at the time it was dissolved.
We will need to file with the Court:
Where the application is made and it is intended that the company will continue trading the Registrar will normally ask for delivery before the Court hearing of any statutory documents required to bring the company's public file up to date.
The Claim Form and supporting evidence must be served on:
The Registrar must be given at least 10 days notice of the hearing to allow her sufficient time to deal with the matter and instruct the Treasury Solicitor.
The Companies Court in London has adopted a practice of attempting where possible to deal with these cases by consent without a formal hearing.
An office copy of the order, with an original Court seal, must be delivered to the Registrar by the Claimant. A company is regarded as being restored when the order is delivered to the Registrar.
In the case of a restoration Order the company is then regarded as having continued in existence as if it had not been struck off and dissolved.
The Treasury Solicitor will usually agree to an application subject to undertakings being given to remedy the default e.g. by filing updated accounts. The Registrar's costs would normally be between £250 to £300, which have to be met by the applicant.
Before the court hearing the Registrar would normally ask for:
Bona Vacantia is property which was owned by a registered company which has been dissolved, then the property is transferred to the Crown as Bona Vacantia.
Bona Vacantia is a Latin expression meaning 'ownerless goods'.
It is only assets which pass to the Crown as Bona Vacantia. When a company is dissolved, any liabilities which the company had are extinguished.
If a Bank account is held in the name of a dissolved company, and the company is still trading, the members should restore the company to the Register if that is possible. If the company is not trading, it has been recognised that restoration is not always an economic proposition. The Treasury Solicitor has therefore been given a limited discretion to make payments to former members and liquidators of the dissolved company out of any money held in the company's Bank account.
If the dissolved company owned either the freehold or the master lease for the building then you could find that you cannot sell your flat. In that situation the usual practice of the Treasury Solicitor is to offer to sell the freehold or the master lease to the tenants jointly, or to a new management company. In the case of leases with a period of at least 60 years to run, the price is ten times the total of the annual ground rents, subject to a minimum of £500 plus costs.
A UK company can only be restored by the registered agent on record, or the agent can be transferred, which we can organise.


text:
HD368 SDI to HDMI Converter-pass through SDI to HDMI converter converts SDI or HD-SDI to HDMI for driving HDMI monitors. Embedded SDI/HD-SDI audio is supported in the HDMI output, allowing a convenient single cable audio/video connection.
Share your reviews, photos and videos here!
Fed 26,2015 11:02:04
Very fast delivery 2 weeks! Thank you! The prouct works well.
Is this feedback helpful for you?Yes ( 0 )No ( 2 )


text:
You must be able to speak Goblish in order to read this book.
Page 1
Page 2
Many times ago, Naggresh wuz da slayer of many halfy deputies. He terrorized da thicket so many, they build great walls to keeps him out. Dis is Naggrsh's wallz for he makes da halfys makes it.
Page 3
Naggresh carried da Great Spear of Many Noggins. Da spear with halfy noggins on it likes pumpkins on da post. He makes da peoples run inside da town and never come out at dark time. At dark times is Naggresh time.
Page 4
Much jumhum was fouled by Naggresh wid no fear of trouble. His greatest funs was to foul it day or night and make da halfys be stinky. No patch was safes, only in da city walls.
Page 5
Of da bestest deeds he do was da stealin of da mayor's golden birdy bird. Naggresh takes da birdy bird and makes cook soup! He eats da birdy bird and shares nones wid da uder gobbies.
Page 6
Naggresh lives da long life wid much glory. He slays da rats and da spiders too. He makes da soup of da snakes and da yellow stingers. He gives dem da great ouches.
Page 7
Da Bixies fear da Naggresh most of all. Many attacks on dem does he make. He takes da sweetie sweets from da bixie birgs and he takes da weapons! Much sweetie sweets does be brings to da uder gobbies.
Page 8
He is da time now we's find da bones of Naggresh. Many gobbi tribes come to da Runnyeye to makes nice with da ghostiez and makes honor for Naggresh, da Terror of da Misty Thicket.
Page 9
Gobbies comes from all every place to bringz gifties for Naggresh! Many of da items were stuff dat was owned by Naggresh. Many itemz have magickly powers and da sacred danglies. Danglies dat bring da lucky to da gobbies!
Page 10
Dis is da stories we telly tells arounds da soup fires. We's makes da dance and pokes da spear and be's Naggresh for all gobbies to sees. We's makes Naggresh a da hero of all gobbies. We's telly tells da stories so's Naggresh makes da glories.


text:
Mary Owen Rosenthal, 88, fine artist and printmaker, passed away peacefully at home in Chicago, April 18, 2017. A native of Huntington WV, she moved to Chicago as a young girl. Her father, a journalist, became a copywriter at the Chicago Tribune and later Managing Editor. Her family then moved to Evanston where she graduated from high school. Formerly of Oberlin and Hudson, OH, she earned her BA from Oberlin College and her MFA from Western Michigan Univ.
Beloved wife of 50+ years to the late Vincent Arthur Rosenthal, loving mother of Susan Lynne Rosenthal of Phoenix, AZ, Garnett Mary Kilberg (Fred) Cohen of Chicago, and Thomas F. Rosenthal of Virginia City, MT; grandmother of James K. Kilberg of Chicago, IL, Wendy (Will Barley) Bartlo of Champaign, IL, and Brett (Amanda Smith) Bartlo of Denver, CO; great-grandmother of Rory Vincent Elias Kilberg and Vivian Mary Barley; sister of Lynne (Pete) Penczer of CT.
Daughter of the late Stewart Douglas Owen and Ida Garnett Osborn Owen.
Memorial service Saturday, May 13, 11:30 am, Graceland Cemetery Chapel, 4001 N. Clark St, Chicago.
In lieu of flowers, donations may be made in her name to Firelands Association for the Visual Arts (FAVA), 39 Main Street in Oberlin, OH, 44074.
Arrangements by Cremation Society of Illinois, 773-281-5058 or www.cremation-society.com.
(Cremation Society of Illinois 773-281-5058 HH 4-23-17)


text:
The Tendo Ryu school of art and Science is one of the regions premier martial arts schools in Memphis Tn.
Free classes are offered the first Sunday of every month. Classes may include throwing and falling practice, joint "locks", timing exercises, and weapons practice. Those interested in Aikido are welcome to attend one of our free classes (first Sunday of every month), or to visit during any regular class. We recommend wearing loose-fitting exercise pants and a t-shirt. All students will need to sign a release form prior to practice. Students under 18 will need a parent or guardian to sign a release form. Regular dues are $75 per month. Undergraduate and high school student dues are $55 per month. (A one-time $100 membership fee is also due when you start).
5:30 PM– 7:00 PM – Open Class
5:15 PM – 6:15 PM – Kids / Junior
6:30 PM – 7:40 PM – All Levels
5:15 PM – 6:15 PM — Kids / Junior
6:30 PM – 8:00 PM – All Levels
4955 Cottonwood Memphis TN
6:30 PM. — 8:00 PM All Levels
call for appointment
(Ninpo)—-Deshi prep class
10:00AM – 12:00 PM – All Levels
Call for an Appointment


text:
Looking for Books in Social Media: An Analysis of Complex Search Requests
- Mabel Reeves
- 2 years ago
- Views:
1 Looking for Books in Social Media: An Analysis of Complex Search Requests Marijn Koolen 1, Toine Bogers 2, Antal van den Bosch 3, and Jaap Kamps 1 1 University of Amsterdam, The Netherlands, 2 Aalborg University Copenhagen, Denmark, 3 Radboud University, Nijmegen, The Netherlands, Abstract. Real-world information needs are generally complex, yet almost all research focuses on either relatively simple search based on queries or recommendation based on profiles. It is difficult to gain insight into complex information needs from observational studies with existing systems; potentially complex needs are obscured by the systems limitations. In this paper we study explicit information requests in social media, focusing on the rich area of social book search. We analyse a large set of annotated book requests from the LibraryThing discussion forums. We investigate 1) the comprehensiveness of book requests on the forums, 2) what relevance aspects are expressed in real-world book search requests, and 3) how different types of search topics are related to types of users, human recommendations, and results returned by retrieval and recommender systems. We find that book search requests combine search and recommendation aspects in intricate ways that require more than only traditional search or (hybrid) recommendation approaches. Keywords: Book Search, Social Media, Evaluation, Recommendation 1 Introduction The rise of social media has had a major impact on how we search for and share information. For instance, it has radically changed the nature of book discovery, which has become easier than ever due to social cataloging sites, such as LibraryThing, GoodReads, Shelfari, BookLamp, Libib, and The Reading Room. We focus on LibraryThing 4 (LT), a popular social cataloguing site. The book collections shared on LT by its 1.8 million members cover over 8 million unique works in total. They describe not only the contents of those books, but also how the books engaged them, what their impact was, and how this related to other reading experiences. LT also offers a popular discussion forum (see Figure 1) for readers to discuss and review books, authors, and literature in general. A prominent use of the LT forum is book discovery: thousands of LT members use the forum to receive or provide recommendations for which books to read next. These book requests display a remarkable breadth, ranging from search-type requests for books on specific topics or for certain moods, to recommendation-type requests for books similar to what a member has already read. 4 last accessed January 11, 2015.
2 The general aim of this paper is to investigate whether explicit information requests in such social media, in particular related to book search, can be used to gain insight in complex information needs, i.e., those that cannot be solved by a straightforward look-up search. We study this in the context of the INEX Social Book Search Track 5 [12, 14, 15]. In recent years, this track has focused on book requests posted on the LT discussion forums. This paper provides a more detailed investigation into the nature of such requests. In the forums anyone can ask for book recommendations for a specific topic and other members reply with book suggestions. These suggestions can be seen both as relevance judgments and recommendations. The search requests go beyond topical relevance [13] and include many subjective aspects such as quality, interestingness, engagement, and familiarity. Cosijn and Ingwersen [7] and Saracevic [20] are among many that argue for the existence of different types of relevance in addition to pure topical relevance, such as situational, motivational, and affective relevance. A comprehensive survey of different interpretations of relevance is given by Borlund [4]. In this paper, we explore the relevance aspects present in the book domain by annotating and analyzing a large set of book requests from the LT forums. We aim to address the following research questions in this paper: RQ1 How comprehensive are book requests on the LT forum in terms of explicit information on the information need, the context of use, and the context of the user? RQ2 What topical and non-topical relevance aspects are present in book search requests on the LT forums? RQ3 How do different types of topics relate to user characteristics, human recommendations, and retrieval and recommender system results? The rest of this paper is organized as follows. Section 2 presents related work, followed by an overview of the rich contextual data about book requests we can extract from the LT discussion forums in Section 3. Section 4 analyses the book requests with respect to the topical and non-topical relevance aspects expressed in them. Section 5 explores how book requests relate to the context of the user, human book recommendations, and retrieval or recommender system results. Finally, in Section 6, we discuss our results and draw conclusions. 2 Related Work The INEX Social Book Search Track [12, 14, 15] investigates book search in collections with both professional metadata and social media content. For evaluation they use book requests on the LT discussion forums as search topics and book suggestions by members as as relevance judgments and recommendations. Koolen et al. [13] observed that these requests are complex and contain non-topical aspects, and found that the forum suggestions are different in nature than editorial relevance judgments with respect to system evaluation. In this paper we focus on the search requests themselves Ross [19] found that readers use a variety of clues to choose books. Reading a book is a substantial investment of time and energy, so readers look for recommendations 5 All the data used in this paper are made available as part of the CLEF/INEX 2014 SBS Track.
3 Fig. 1. Book request on the LibraryThing forum from trusted sources for selection. Reuter [18] studied book selection by children and identify a list of 46 factors influencing their choices. Buchanan and McKay [5] investigated search activities of customers in bookshops. They find that enquiries often arise from cultural context reading with others, references and reviews in media and argue that customers mental models may deviate from the standard bibliographic metadata. Cunningham et al. [8] studied collaborative information behaviour in bookshops. They found that groups of customers use many different ways to share information about books, e.g., talking aloud, pointing, reading, and searching together, and that they use these interactions to achieve agreement on which books to select. The gap between their mental model and the access points for online book collections may be why users turn to the LT forum for requests. A considerable amount of related work exists on forum search, where the focus is typically on retrieving results from the collection of threads in a single forum. Examples of such approaches include work by Elsas and Carbonell [10] and Bhatia and Mitra [3]. In contrast, we analyze the initial forum posts describing a user s information need, in order to perform cross-collection search using these need descriptions. Our overall aim to use the forums to shed light on complex search requests, their context and relevance aspects, is related to a wealth of studies in information seeking. Some of the most comprehensive earlier studies predate the web and modern search systems (e.g., [21]). Our general approach is to tap into a new source of evidence for researching complex information seeking behavior. 3 Book Search Requests in LT Forums In this section, we investigate RQ1: How comprehensive are book requests on the LT forum in terms of explicit information on the information need, the context of use, and the context of the user? The LT discussion forums are used to discuss a broad range of topics, most of which are book-related. Many members turn to this forum asking for book suggestions and other members can reply and provide suggestions. In a random sample of 500 posts we
4 found 67 (13.4%) containing an explicit book request. Given the massive scale of the forums with nearly 5 million messages and 3.5 million identified book mentions, this gives us access to a huge supply of real world complex search requests. 6 For the more straightforward search tasks, LT users are likely to use book search engines available at e.g. LT, Amazon, or libraries. In contrast, the forum requests contain more complex search needs that LT members have, expressed in natural language. For instance, the request in Figure 1 is highly complex, providing requirements about the content as well as examples of books and authors that the poster is already familiar with, and contextual cues on usage. The user name links to the profile of the user, which provides additional context such as their personal book catalogue. The example books mentioned introduce a form of query-by-example that could also be seen as a recommendation task. These forum threads provide us with an unobtrusive method of investigating realistic, complex search requests that go well beyond traditional query log analysis. Members are not limited by the functionalities of a search engine or recommender system when expressing their request, but only by the concreteness of their information need and their ability to express it in natural language. As a result, they typically leave rich descriptions of their information need as well as many contextual clues to ensure others can understand its complexity. Moreover, the LT forums allow users to mark up the names of books and authors through a simple wiki-like syntax using so-called touchstones. The system then automatically identifies the correct book/author and links the marked-up text to the right LT entity. These suggestions are a form of human relevance judgements. Summarizing, from the forums we can derive rich statements of requests, including explicit statements on the context of use and the context of the user, with example books and ground truth human recommendations. We find that such forum data give a unique opportunity to study complex search requests, and that the requests exhibit an amazing variation in topical and non-topical aspects. This prompt us to investigate what relevance aspects are used in the next section. 4 Relevance in Forum Book Search In this section we study RQ2: What topical and non-topical relevance aspects are present in book search requests on the LT forums? 4.1 Relevance Aspects Our first step is to investigate the complexity of these book search requests and the kind of relevance aspects expressed in them. Reuter [18] collected data from a user study in a children s library and identified 46 aspects, grouped into seven broad categories. We use those categories as our guide for analyzing the relevance aspects of book search requests. Due to its prominence in the LT forums, we introduce known-item search as an additional aspect. This resulted in the following eight relevance aspects: Accessibility The language, length, or level of difficulty of a book. Content Topic, plot, genre, style, or readability of a book. 6 https://www.librarything.com/zeitgeist, last accessed on January 11, 2014.
5 Engagement Affective types of reading experiences evoked by books. Familiarity Books similar to known books or related to a previous experience. Known-item Descriptions of known books to identify the title and/or author. Metadata Aspects like title, author, publication year and format. Novelty Books that are unusual or quirky, or have novel content. Socio-cultural Books related to the user s socio-cultural background or values, have (had) a particular cultural or social impact, or are popular or obscure. 4.2 Annotating Book Search Requests To determine how prominent these different relevance aspects are on the LT forums, we annotated a sample of topic threads for relevance and other characteristics. We selected forum threads likely to contain requests for book recommendations using a simple regular-expression-based classifier, which filtered out all topics that did not contain one or more trigger expressions, such as suggest, looking for and which books. This resulted in a set of 9,403 topic threads containing touchstones. A random set of 2,646 of these topics were annotated by eight different Information Science students, three from the Royal School of Library and Information Science in Copenhagen, three from the Oslo and Akershus University of Applied Sciences, and two from Aalborg University Copenhagen. Each topic was annotated by a single annotator. We created a Web interface to help our annotators (1) identify topic threads as either book requests or non-requests; (2) annotate the requests by which relevance aspect(s) they express; and (3) annotate the suggestions provided by other LT members in the thread. This task included questions on whether the suggestion providers appeared to have read the suggested books and whether their recommendation was positive, negative, or neutral. Of the 2,646 topics annotated by the students, 944 topics (36%) were identified as containing a book request (recall that 13.4% of a random sample contained book requests). For each identified book request, annotators could specify multiple relevance aspects. For example, for topic 99,309 on the politics of multiculturalism (partly shown in Figure 1), the topic starter asks for suggestions about a particular topic (content relevance), but also asks for books similar to what he has already read on the topic (familiarity), but written in a less annoying style (engagement). 4.3 Analysis The distribution of relevance aspects in our annotated set of 944 book requests is shown in the left half of Table 1. The majority of book search information needs on the LT forums express content aspects (698 topics or 74%). Familiarity is the second most frequent aspect at 36%. These two aspects are often combined in a single book request: 267 topics (28%) express both aspects. An example of such a request is Can someone recommend a book that has all the joy, charm, numerous characters, pathos, adventure, love of language, etc. that the novel David Copperfield has? (topic 10392). The searcher wants recommendations based on the book David Copperfield, but also describes aspects of the book to base these recommendations on. This is querying by example as well as description, which is a form of querying that is not supported by any current systems.
6 Table 1. Aspect distribution and overlap in the 944 forum topics (left side) and the conditional probability P(column row) (right side) Aspect overlap Conditional probability A C E F K M N S A C E F K M N S Accessibility Content Engagement Familiarity Known-item Metadata Novelty Socio-cultural Other frequently labeled aspects include metadata (28%), engagement (23%), and known-item (21%). On the LT forum, metadata is an interesting aspect. When searching a catalog, metadata is often used to find specific books or books by a certain author, but such straightforward lookup tasks are not typically posted on the forums. Of the 264 topics labelled with metadata, only 22 (8%) have no other relevance aspect. These topics typically ask for recommendations on which books to read from specific authors, publishers, or series, or for the proper sequence in which to read a set of books. In most cases, metadata is combined with other aspects, and is used to focus the suggestions. Engagement is something that is hard to express through a search engine query. For instance, how can a user search for text books that are funny or for books that challenge the reader s own views on a topic? Such complex relevance criteria may be a reason to ask for suggestions on the LT forum. The same holds for known-item topics where the user can only recall certain elements of the plot or attributes of certain characters. Most book search services are of limited use for such known-item topics, as they do not allow full-text search. Forum members, however, may be able to help out with such requests. Accessibility, novelty and socio-cultural aspects are less prominent in our sample. The rest of Table 1 shows the distribution of the relevance aspects and their cooccurences. We can see a pattern emerging of relevance aspects being combined with either content, familiarity, or both, forming groups of topics clustered around these two aspects. Known-item requests are an exception as they seem to be a separate group. Content requests tend to be more typical of search tasks, as they provide a specific description of the desired books. The familiarity aspect seems related to recommendationoriented tasks. The other aspects are more contextual in nature: dealing with books for certain scenarios (e.g., waiting at an airport, selecting reading material for a book club), for certain age groups or personality traits (e.g., trying to get a spouse to pick up reading), or certain moods (e.g., books that are comforting or challenge ones views). Dealing with such contextual information is an active research topic for both search [9] and recommender systems [1]. Summarizing, in a large sample of book requests annotated by their relevance aspects we find that most requests combine multiple aspects. We observed the largest clusters around content and familiarity aspects, or both, and the known-item class. In
7 Table 2. Topic groups in terms of example books and requested prose genre Feature KI Cx F Co+F Co All Example books Genre Fiction Non-fiction Mix Uncertain the next section, we will divide the requests into different groups based on these relevance aspects and study them in more detail. 5 Impact of Content and/or Familiarity In this section, we investigate RQ3: How do different types of topics relate to user characteristics, human recommendations, and retrieval and recommender system results? 5.1 Grouping Topics on Relevance Aspects In the previous section we saw a prevalence of content and familiarity aspects, in isolation and in combination, suggesting a grouping of the requests based on these relevance aspects. With the known-item requests as a separate group and the four logical combinations of content and familiarity aspects, this results in the following five topic groups: Known-item (KI) contains all 202 known-item topics. This is the most content-specific information need, but different from the rest in that the user wants a specific book. Context (Cx) contains all 78 topics without content, familiarity, or known-item. There are no content-based aspects on which to base document similarity. Familiarity (F) contains 66 topics with familiarity, but no content. Users search for books similar to a specific (set of) book(s) or genre(s). Document similarity is underspecified, i.e., the user gives no content aspects to base similarity on. Content and Familiarity (Co+F) contains 260 topics with both content and familiarity aspects, articulating explicit and implicit topic aspects. The similarity of the desired books is expressed at the level of books as well as at the finer-grained level of specific textual aspects of the books. Content (Co) contains 338 topics with content, but no familiarity. Users are searching for books matching specific content aspects. Here, document similarity is more explicit, corresponding to a more specific information need. 5.2 Analysis of Genre, Popularity, and Personal Catalogues To understand how our groupings correspond to actual differences in the nature of topic groups, we compare them on characteristics of the request, the requester, and the suggested books: (1) the presence of example books in touchstones, (2) the genre of books they target, (3) the size of the requester s book catalogues, and (4) the popularity of books in the requester s catalogue or those suggested at the forum.
8 Table 3. Catalogue size in requester catalogue (median of each topic group) Feature KI Cx F Co+F Co All Pre-topic Post-topic Total Providing Example Books For some topics, requesters add example books to their initial post using touchstones. These examples can serve different purposes: (1) positive examples of what they want (more of); (2) negative examples that match some relevance aspect(s), but not all; or (3) examples of what they have already read. Out of the 944 topics in total, only 256 (27%) have example books in the initial request, as shown in Table 2. We expect that examples are common among F topics based on previous reading experiences, and rare among KI and Co topics. These expectations are supported by the relevance aspects: the majority of the F topics include examples (54%), whereas only 8% of the KI topics contain examples. This lends credence to our decision to split the topics into groups based on the content and familiarity aspects. Genre Our annotators indicated whether requests were for fiction, non-fiction, or both. Table 2 shows that, of the 944 topics in total, 469 (50%) asked for suggestions on fiction books, 150 (16%) on non-fiction, and 113 (12%) on both fiction and non-fiction. For 212 topics (22%) the annotator could not tell. Fiction was the most common prose genre for KI topics at 77%, whereas only 6% of the topics were non-fiction. Fiction was also common for the F group at 53%. Cx topics have no specific content aspects, so it makes sense that mixed-genre topics and ambiguous topics are more common. In contrast, the Co topics are focused on non-fiction books more frequently than the other topic groups. Intuitively, this makes sense, as the topical content is arguably the main reason for reading a non-fiction book. Requests for fiction books are more likely to refer to examples, because what one is looking for in fiction may be more difficult to express and less explicitly related to the topical content of the book. This provides further evidence that the criteria for the topic groups are meaningful for analysis. Cataloguing behavior Next, we count how many books the topic creator catalogued before posting the request (pre-topic), after posting it (post-topic), and in total; results are listed in Table 3. KI topics are often posted by LT members who have no books in their catalogue. Private profiles are an unlikely explanation for this, as these are rare. It seems these LT members use the forums mainly as a search engine and discussion board instead of as a tool for managing their book collections. Requesters of Cx topics tend to have small pre-topic catalogues, but add more books afterwards. These may be relatively new users with limited reading experience and have difficulty describing in detail what books they are looking for. Instead, they describe the context in which they want to read books. F and Co+F topics tend to come from more active users who have over 100 books pre-topic and remain active cataloguers post-topic. This suggests they know what they like and that their needs have become more specific, but are still broad enough that they only need to implicitly describe what they want by giving examples. We speculate that users with Co topics are typically heavy readers, who have large pre-
9 Table 4. Median book popularity in requester catalogue, forum suggestions, system results Feature KI Cx F Co+F Co All Requester catalogue topic group median topic group mean topic group std.dev Forum suggestions topic group median Retrieval Top Top Recommender Top Top topic catalogues and remain very active users. They can explicitly describe what they are looking for and may in fact leave out examples to avoid responders from picking up on the wrong similarity clues from those examples. Book Popularity Chandler [6] examined the different strategies of GoodReads users for discovering new books to read and how these relate to the popularity of discovered books. They found that the popularity distribution of books discovered through search has a long tail of less popular books, whereas for GoodReads recommendations the distribution is concentrated around the mid- to high-popularity books. How are the five topic groups related to the popularity of books discussed on the LT forums? The popularity Pop(d) of a book d is the number of users who have d in their catalogues in our profile crawl. The top half of Table 4 shows the median popularity of books in searchers catalogues and the forum suggestions. The catalogues of requesters tend to have a mix of popular and obscure books the topic group mean is higher than the median indicating the distribution is skewed with a minority of highly popular books. There is no big difference between the popularity distributions of requesters with F, Co and Co+F topics. For the forum suggestions we see larger relative differences between topic groups, however. Forum members suggest more popular books for Cx and F topics than for Co and Co+F topics. The popularity of suggested books diminishes as content-specificity increases. For KI topics the group with arguably the highest content specificity suggestions are even less popular. Relating this to the findings of Chandler [6], suggestions for Co and Co+F topics are closer to search-related discoveries and F and Cx closer to recommendation-related discoveries. In terms of suggestions, book search on the LT forums seems to have a mix of search and recommendationoriented tasks. 5.3 Retrieval and Recommendation Results We analyse the books returned by standard retrieval and recommender systems for the forum topics, and compare them to the actual suggestions given by LT forum members. For the retrieval system, we use the Amazon/LibraryThing collection [2] that is also used in the INEX Social Book Search Track [15]. This collection contains book metadata for 2.8 million books, including formal metadata (title, author, publisher, publication date), professional subject metadata (subject headings, Dewey Decimal System codes) and user-generated content (Amazon user reviews, LT user tags). We use Indri
10 Table 5. Performance evaluation of retrieval, recommender and best case fusion results KI λ Cx λ F λ Co+F λ Co λ All λ Retrieval Recommendation Fusion MRR Retrieval Recommendation Fusion [11] and index all content with Krovetz stemming and stopword removal using a list of 319 stopwords. Specifically, we use a standard Language Model run with Dirichlet smoothing (µ = 2500), using a combination of the thread title, a query provided by the annotators and the name of the discussion group as a query. This combination gives the best performance with standard Language Model settings. For the recommender system we use a set of 84,210 user profiles, with information on which books the user catalogued and when, to compute nearest neighbours. We represent each user by a vector of book IDs and compute tf idf similarity using GenSim [17]. The recommendation score for a book is the sum of the similarities of the individual neighbours who catalogued that book. We use a standard k-nn model run with recommendations from the 100 nearest neighbours based on catalogue similarity. The lower half of Table 4 shows the median book popularity of returned results of the two runs. The rankings show a strong popularity effect for the recommender system, with the retrieval systems picking up less popular books in general than the recommender system. The popularity effect of recommender systems is also known as the Harry Potter problem [16]. The top of the rankings show relative differences between content and familiarity topics, with especially recommender systems returning more popular books for F topics than for Co+F and Co topics. The query terms of Co topics target less popular books than query terms of F topics, and their users are similar to users with a smaller fraction of highly popular books in their catalogues. In terms of book popularity, forum suggestions are roughly as popular as retrieval results. This is in line with earlier results on recommendations from friends as found on GoodReads [6]. Even though forum members do not know the requester personally, the statement of request is comprehensive enough for them to target the right types of books. Finally, we look at system performance of the retrieval and recommender systems on the forum requests and suggestions, based on the Qrels from the INEX 2014 SBS Track for evaluation. We focus on basic retrieval and recommender models to observe relative performance of the two approaches on the various request types under well-understood conditions. In future work we will explore different models and query and user models in more detail. In addition to the baseline, we assess the potential of hybrid systems merging the results lists of both baselines using a weighted sum, S fusion (d, q) = λ S retrieval (d, q) + (1 λ) S recommendation (d, q). The performance scores are shown in Table 5. As expected, the retrieval system outperforms the recommender system, but there are differences between the topic groups. The recommender system
11 scores relatively well on the Cx, F and Co+F topics, while the retrieval system performs relatively better on KI and Co than on Cx and Co+F. More importantly, on all topic groups, fusion of the results lists leads to improvements.topics with familiarity or non-content aspects show the largest relative improvement. Finally, as a combination of recommendation and retrieval aspects, the Co+F topic set shows through its λ value of 0.6 that a more balanced fusion produces the best results. This suggests that the type of request plays an important role in the design of book discovery systems. Summarizing, we analyzed topic groups related to known-item search and the logical combinations of content and/or familiarity. We observed varying degrees of combinations of contextual search and recommendation aspects. In terms of cataloguing behaviour, the content-specificity of requests is related to the size of the requester s catalogue. In terms of popularity, forum suggestions for topics with content aspects are more similar to retrieval results, and those for topics with familiarity aspects more similar to recommendation results. We demonstrated there is room for improvement by combining retrieval and recommendation approaches. 6 Conclusions The aim of this paper was to investigate complex search requests in social media, in particular focusing on book search as observed on the LibraryThing discussion forums. First, we found that the LT forums provide an unobtrusive way to study realistic, complex book search requests, which show a broad variation in topical and contextual relevance aspects. Second, we annotated the relevance aspects expressed in book requests at the LT forums. We found that the two dominating aspects are the content of the book and looking for familiar reading experiences, while other aspects are more oriented toward the reading context. The combination of content, context, and examples in a search request is a form of querying that is not supported by any current systems. Third, we found that these topic groups based on content and familiarity aspects can be differentiated by whether the requesters provide example books, what genre they are looking for (fiction or non-fiction), their cataloging activity, and the popularity of the suggested books. Retrieval systems can effectively use the content aspects of the search requests, and recommender systems can pick up signals in the requester s catalogue. We demonstrated the possibility for improvement when combining both approaches, in particular for topic groups where context and familiarity play a role. This suggest that the request type has an important role to play in the design of book discovery systems. Our analysis was focused on the book search domain, yet similar rich profiles and contextual information is available in many modern search scenarios, in particular in mobile search and increasingly aggregated to mixed-device search scenarios. Research access to such mobile search logs and social media data is difficult due to privacy and commercial constraints, making the more constrained and less sensitive book search domain an attractive alternative to study many aspects of complex contextualized search. We highlighted the diversity of complex search requests, and observed a mixture of content and context going beyond currently existing systems. This is an important first step toward the development of novel information access systems that blend traditional search and (hybrid) recommendation approaches into a coherent whole.
12 Acknowledgments This research was supported by the Netherlands Organization for Scientific Research (README project, NWO VIDI # ; ExPoSe project, NWO CI # ). References [1] S. Anand and B. Mobasher. Contextual Recommendation. In From Web to Social Web: Discovering and Deploying User and Content Profiles, volume 4737 of LNCS, pages Springer, [2] T. Beckers, N. Fuhr, N. Pharo, R. Nordlie, and K. N. Fachry. Overview and Results of the INEX 2009 Interactive Track. In Proceedings of ECDL 10, pages , [3] S. Bhatia and P. Mitra. Adopting Inference Networks for Online Thread Retrieval. In Proceedings of AAAI 10. AAAI Press, [4] P. Borlund. The Concept of Relevance in IR. JASIST, 54(10): , [5] G. Buchanan and D. McKay. In the bookshop: examining popular search strategies. In Proceedings of JCDL 11, pages ACM, [6] O. Chandler. How Consumers Discover Books Online. In Tools of Change for Publishing. O Reilly, [7] E. Cosijn and P. Ingwersen. Dimensions of Relevance. Information Processing & Management, 36: , [8] S. J. Cunningham, N. Vanderschantz, C. Timpany, A. Hinze, and G. Buchanan. Social Information Behaviour in Bookshops: Implications for Digital Libraries. In Proceedings of TPDL 13, volume 8092 of LNCS, pages Springer, [9] A. Dean-Hall, C. L. A. Clarke, J. Kamps, P. Thomas, N. Simon, and E. Voorhees. Overview of the TREC 2013 contextual suggestion track. In Proceedings of TREC 13. NIST, [10] J. L. Elsas and J. G. Carbonell. It Pays to be Picky: An Evaluation of Thread Retrieval in Online Forums. In Proceedings of SIGIR 09, pages ACM, [11] Indri. Language modeling meets inference networks, projects/lemur/. [12] M. Koolen, G. Kazai, J. Kamps, M. Preminger, A. Doucet, and M. Landoni. Overview of the INEX 2012 Social Book Search Track. In Proceedings of INEX 12, LNCS. Springer. [13] M. Koolen, J. Kamps, and G. Kazai. Social Book Search: Comparing Topical Relevance Judgements and Book Suggestions for Evaluation. In Proceedings of CIKM 12, pages ACM, [14] M. Koolen, G. Kazai, J. Kamps, A. Doucet, and M. Landoni. Overview of the INEX 2011 books and social search track. In Proceedings of INEX 11, volume 7424 of LNCS. Springer, [15] M. Koolen, G. Kazai, M. Preminger, and A. Doucet. Overview of the INEX 2013 social book search track. In Proceedings of the CLEF 2013 Evaluation Labs and Workshop, [16] G. Linden. Geeking with Greg: Early Amazon: Similarities, blogspot.nl/2006/03/early-amazon-similarities.html. [17] R. Řehůřek and P. Sojka. Software Framework for Topic Modelling with Large Corpora. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pages ELRA, [18] K. Reuter. Assessing aesthetic relevance: Children s book selection in a digital library. JASIST, 58(12): , [19] C. S. Ross. Finding without seeking: the information encounter in the context of reading for pleasure. Information Processing & Management, 35(6): , [20] T. Saracevic. Relevance Reconsidered. In Proceedings of COLIS 2, pages , [21] T. Saracevic and P. B. Kantor. A Study of Information Seeking and Retrieving. I, II, III. JASIS, 39(3): , 1988.
Effective Metadata for Social Book Search from a User Perspective
Effective Metadata for Social Book Search from a User Perspective Hugo Huurdeman 1,2, Jaap Kamps 1,2,3, and Marijn Koolen 1 1 Institute for Logic, Language and Computation, University of Amsterdam 2 ArchivesMore information
Search and Information Retrieval
Search and Information Retrieval Search on the Web 1 is a daily activity for many people throughout the world Search and communication are most popular uses of the computer Applications involving searchMore information
Term extraction for user profiling: evaluation by the user
Term extraction for user profiling: evaluation by the user Suzan Verberne 1, Maya Sappelli 1,2, Wessel Kraaij 1,2 1 Institute for Computing and Information Sciences, Radboud University Nijmegen 2 TNO,More information
Query term suggestion in academic search
Query term suggestion in academic search Suzan Verberne 1, Maya Sappelli 1,2, and Wessel Kraaij 2,1 1. Institute for Computing and Information Sciences, Radboud University Nijmegen 2. TNO, Delft Abstract.More information
Data Mining Yelp Data - Predicting rating stars from review text
Data Mining Yelp Data - Predicting rating stars from review text Rakesh Chada Stony Brook University email@example.com Chetan Naik Stony Brook University firstname.lastname@example.com ABSTRACT The majorityMore information
Improving Contextual Suggestions using Open Web Domain Knowledge
Improving Contextual Suggestions using Open Web Domain Knowledge Thaer Samar, 1 Alejandro Bellogín, 2 and Arjen de Vries 1 1 Centrum Wiskunde & Informatica, Amsterdam, The Netherlands 2 Universidad AutónomaMore information
Comparing Tag Clouds, Term Histograms, and Term Lists for Enhancing Personalized Web Search
Comparing Tag Clouds, Term Histograms, and Term Lists for Enhancing Personalized Web Search Orland Hoeber and Hanze Liu Department of Computer Science, Memorial University St. John s, NL, Canada A1B 3X5More information
Visually Exploring Books along their Subject Headings
Visually Exploring Books along their Subject Headings Cody Coljee-Gray 1, Marian Dörk 2 and Sheelagh Carpendale 1 1 University of Calgary 2 University of Applied Sciences Potsdam Abstract We present aMore information
Purposes and Processes of Reading Comprehension
2 PIRLS Reading Purposes and Processes of Reading Comprehension PIRLS examines the processes of comprehension and the purposes for reading, however, they do not function in isolation from each other orMore information
Building a Book Recommender system using time based content filtering
Building a Book Recommender system using time based content filtering CHHAVI RANA Department of Computer Science Engineering, University Institute of Engineering and Technology, MD University, Rohtak,More information
Record Tagging for Microsoft Dynamics CRM
Record Tagging for Microsoft Dynamics CRM An easy and intuitive way to apply tags to records in CRM Why tagging? Tagging has become a very normal way for content creators and consumers to apply unstructuredMore information
Exploring Adaptive Window Sizes for Entity Retrieval
Exploring Adaptive Window Sizes for Entity Retrieval Fawaz Alarfaj, Udo Kruschwitz, and Chris Fox School of Computer Science and Electronic Engineering University of Essex Colchester, CO4 3SQ, UK {falarf,udo,email@example.com information
Reverse Engineering of Relational Databases to Ontologies: An Approach Based on an Analysis of HTML Forms
Reverse Engineering of Relational Databases to Ontologies: An Approach Based on an Analysis of HTML Forms Irina Astrova 1, Bela Stantic 2 1 Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn,More information
SIGIR 2004 Workshop: RIA and "Where can IR go from here?"
SIGIR 2004 Workshop: RIA and "Where can IR go from here?" Donna Harman National Institute of Standards and Technology Gaithersburg, Maryland, 20899 email@example.com Chris Buckley Sabir Research, Inc.More information
So today we shall continue our discussion on the search engines and web crawlers. (Refer Slide Time: 01:02)
Internet Technology Prof. Indranil Sengupta Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur Lecture No #39 Search Engines and Web Crawler :: Part 2 So today weMore information
Site-Specific versus General Purpose Web Search Engines: A Comparative Evaluation
Panhellenic Conference on Informatics Site-Specific versus General Purpose Web Search Engines: A Comparative Evaluation G. Atsaros, D. Spinellis, P. Louridas Department of Management Science and TechnologyMore information
The University of Amsterdam s Question Answering System at QA@CLEF 2007
The University of Amsterdam s Question Answering System at QA@CLEF 2007 Valentin Jijkoun, Katja Hofmann, David Ahn, Mahboob Alam Khalid, Joris van Rantwijk, Maarten de Rijke, and Erik Tjong Kim Sang ISLA,More information
Predicting IMDB Movie Ratings Using Social Media
Predicting IMDB Movie Ratings Using Social Media Andrei Oghina, Mathias Breuss, Manos Tsagkias, and Maarten de Rijke ISLA, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The NetherlandsMore information
Semantic Search in Portals using Ontologies
Semantic Search in Portals using Ontologies Wallace Anacleto Pinheiro Ana Maria de C. Moura Military Institute of Engineering - IME/RJ Department of Computer Engineering - Rio de Janeiro - Brazil [awallace,anamoura]@de9.ime.eb.brMore information
Recommendation Tool Using Collaborative Filtering
Recommendation Tool Using Collaborative Filtering Aditya Mandhare 1, Soniya Nemade 2, M.Kiruthika 3 Student, Computer Engineering Department, FCRIT, Vashi, India 1 Student, Computer Engineering Department,More information
A Search Log-Based Approach to Evaluation
A Search Log-Based Approach to Evaluation Junte Zhang 1 and Jaap Kamps 1,2 1 Archives and Information Studies, Faculty of Humanities, University of Amsterdam 2 ISLA, Faculty of Science, University of AmsterdamMore information
Network Big Data: Facing and Tackling the Complexities Xiaolong Jin
Network Big Data: Facing and Tackling the Complexities Xiaolong Jin CAS Key Laboratory of Network Data Science & Technology Institute of Computing Technology Chinese Academy of Sciences (CAS) 2015-08-10More information
Resolving Common Analytical Tasks in Text Databases
Resolving Common Analytical Tasks in Text Databases The work is funded by the Federal Ministry of Economic Affairs and Energy (BMWi) under grant agreement 01MD15010B. Database Systems and Text-based InformationMore information
Towards Inferring Web Page Relevance An Eye-Tracking Study
Towards Inferring Web Page Relevance An Eye-Tracking Study 1, email@example.com Yinglong Zhang 1, firstname.lastname@example.com 1 The University of Texas at Austin Abstract We present initial results from a project,More information
Brunswick, NJ: Ometeca Institute, Inc., 2005. 617 pp. $35.00. (0-9763547-0-5).
Information Retrieval Design. James D. Anderson and Jose Perez-Carballo. East Brunswick, NJ: Ometeca Institute, Inc., 2005. 617 pp. $35.00. (0-9763547-0-5). Information Retrieval Design is a textbook thatMore information
recommendation in e-commerce
recommendation in e-commerce Luminis Recommendation Services We are leaving the age of information and entering the age of recommendation Chris Anderson Authors: Hans Bossenbroek Hans Gringhuis LuminisMore information
Towards a Visually Enhanced Medical Search Engine
Towards a Visually Enhanced Medical Search Engine Lavish Lalwani 1,2, Guido Zuccon 1, Mohamed Sharaf 2, Anthony Nguyen 1 1 The Australian e-health Research Centre, Brisbane, Queensland, Australia; 2 TheMore information
Big Data Analytics of Multi-Relationship Online Social Network Based on Multi-Subnet Composited Complex Network
, pp.273-284 http://dx.doi.org/10.14257/ijdta.2015.8.5.24 Big Data Analytics of Multi-Relationship Online Social Network Based on Multi-Subnet Composited Complex Network Gengxin Sun 1, Sheng Bin 2 andMore information
Understanding Book Search Behavior on the Web
Understanding Book Search Behavior on the Web Jin Young Kim Dept. of Computer Science University of Massachusetts Amherst, MA 01003 firstname.lastname@example.com Henry Feild Dept. of Computer Science University ofMore information
Web Archiving and Scholarly Use of Web Archives
Web Archiving and Scholarly Use of Web Archives Helen Hockx-Yu Head of Web Archiving British Library 15 April 2013 Overview 1. Introduction 2. Access and usage: UK Web Archive 3. Scholarly feedback onMore information
Interoperability, Standards and Open Advancement
Interoperability, Standards and Open Eric Nyberg 1 Open Shared resources & annotation schemas Shared component APIs Shared datasets (corpora, test sets) Shared software (open source) Shared configurationsMore information
Incorporating Window-Based Passage-Level Evidence in Document Retrieval
Incorporating -Based Passage-Level Evidence in Document Retrieval Wensi Xi, Richard Xu-Rong, Christopher S.G. Khoo Center for Advanced Information Systems School of Applied Science Nanyang TechnologicalMore information
STATISTICA. Financial Institutions. Case Study: Credit Scoring. and
Financial Institutions and STATISTICA Case Study: Credit Scoring STATISTICA Solutions for Business Intelligence, Data Mining, Quality Control, and Web-based Analytics Table of Contents INTRODUCTION: WHATMore information
Overview of iclef 2008: search log analysis for Multilingual Image Retrieval
Overview of iclef 2008: search log analysis for Multilingual Image Retrieval Julio Gonzalo Paul Clough Jussi Karlgren UNED U. Sheffield SICS Spain United Kingdom Sweden email@example.com email@example.com information
A Business Process Services Portal
A Business Process Services Portal IBM Research Report RZ 3782 Cédric Favre 1, Zohar Feldman 3, Beat Gfeller 1, Thomas Gschwind 1, Jana Koehler 1, Jochen M. Küster 1, Oleksandr Maistrenko 1, AlexandruMore information
Text Mining - Scope and Applications
Journal of Computer Science and Applications. ISSN 2231-1270 Volume 5, Number 2 (2013), pp. 51-55 International Research Publication House http://www.irphouse.com Text Mining - Scope and Applications MissMore information
Microsoft FAST Search Server 2010 for SharePoint Evaluation Guide
Microsoft FAST Search Server 2010 for SharePoint Evaluation Guide 1 www.microsoft.com/sharepoint The information contained in this document represents the current view of Microsoft Corporation on the issuesMore information
Designing an Adaptive Virtual Guide for Web Applications
6th ERCIM Workshop "User Interfaces for All" Long Paper Designing an Adaptive Virtual Guide for Web Applications Luisa Marucci, Fabio Paternò CNUCE-C.N.R. Via V.Alfieri 1, 56010 Ghezzano - Pisa, ItalyMore information
Scholarly Use of Web Archives
Scholarly Use of Web Archives Helen Hockx-Yu Head of Web Archiving British Library 15 February 2013 Web Archiving initiatives worldwide http://en.wikipedia.org/wiki/file:map_of_web_archiving_initiatives_worldwide.pngMore information
Annotea and Semantic Web Supported Collaboration
Annotea and Semantic Web Supported Collaboration Marja-Riitta Koivunen, Ph.D. Annotea project Abstract Like any other technology, the Semantic Web cannot succeed if the applications using it do not serveMore information
Search Taxonomy. Web Search. Search Engine Optimization. Information Retrieval
Information Retrieval INFO 4300 / CS 4300! Retrieval models Older models» Boolean retrieval» Vector Space model Probabilistic Models» BM25» Language models Web search» Learning to Rank Search Taxonomy!More information
Universities of Leeds, Sheffield and York http://eprints.whiterose.ac.uk/
promoting access to White Rose research papers Universities of Leeds, Sheffield and York http://eprints.whiterose.ac.uk/ This is an author produced version of a paper published in Advances in InformationMore information
What to Expect on the Compass
What to Expect on the Compass What is the Compass? COMPASS is a set of untimed computer adaptive tests created by the American College Test (ACT) Program. Because COMPASS tests are "computer adaptive,"More information
Information Need Assessment in Information Retrieval
Information Need Assessment in Information Retrieval Beyond Lists and Queries Frank Wissbrock Department of Computer Science Paderborn University, Germany email@example.com Abstract. The goal of every informationMore information
User Data Analytics and Recommender System for Discovery Engine
User Data Analytics and Recommender System for Discovery Engine Yu Wang Master of Science Thesis Stockholm, Sweden 2013 TRITA- ICT- EX- 2013: 88 User Data Analytics and Recommender System for DiscoveryMore information
Understanding Web personalization with Web Usage Mining and its Application: Recommender System
Understanding Web personalization with Web Usage Mining and its Application: Recommender System Manoj Swami 1, Prof. Manasi Kulkarni 2 1 M.Tech (Computer-NIMS), VJTI, Mumbai. 2 Department of Computer Technology,More information
The Scientific Data Mining Process
Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] InMore information
Data Warehouse and Business Intelligence Testing: Challenges, Best Practices & the Solution
Warehouse and Business Intelligence : Challenges, Best Practices & the Solution Prepared by datagaps http://www.datagaps.com http://www.youtube.com/datagaps http://www.twitter.com/datagaps Contact email@example.com information
Evaluating a new programming language
In G. Kadoda (Ed). Proc. PPIG 13 Pages 275-289 Evaluating a new programming language Steven Clarke Microsoft Corporation 1 Microsoft Way Redmond, WA 98052 USA +1 425 705 5978 email@example.com Keywords:More information
Generating SQL Queries Using Natural Language Syntactic Dependencies and Metadata
Generating SQL Queries Using Natural Language Syntactic Dependencies and Metadata Alessandra Giordani and Alessandro Moschitti Department of Computer Science and Engineering University of Trento Via SommariveMore information
131-1. Adding New Level in KDD to Make the Web Usage Mining More Efficient. Abstract. 1. Introduction [1]. 1/10
1/10 131-1 Adding New Level in KDD to Make the Web Usage Mining More Efficient Mohammad Ala a AL_Hamami PHD Student, Lecturer m_ah_1@yahoocom Soukaena Hassan Hashem PHD Student, Lecturer soukaena_hassan@yahoocomMore information
A Better Statistical Method for A/B Testing in Marketing Campaigns
A Better Statistical Method for A/B Testing in Marketing Campaigns Scott Burk Marketers are always looking for an advantage, a way to win customers, improve market share, profitability and demonstrateMore information
Creating Synthetic Temporal Document Collections for Web Archive Benchmarking
Creating Synthetic Temporal Document Collections for Web Archive Benchmarking Kjetil Nørvåg and Albert Overskeid Nybø Norwegian University of Science and Technology 7491 Trondheim, Norway Abstract. InMore information
Towards a Transparent Proactive User Interface for a Shopping Assistant
Towards a Transparent Proactive User Interface for a Shopping Assistant Michael Schneider Department of Computer Science, Saarland University, Stuhlsatzenhausweg, Bau 36.1, 66123 Saarbrücken, Germany email@example.com information
The Cost of Annoying Ads
The Cost of Annoying Ads DANIEL G. GOLDSTEIN Microsoft Research and R. PRESTON MCAFEE Microsoft Corporation and SIDDHARTH SURI Microsoft Research Display advertisements vary in the extent to which theyMore information
Using Contextual Information to Improve Search in Email Archives
Using Contextual Information to Improve Search in Email Archives Wouter Weerkamp, Krisztian Balog, and Maarten de Rijke ISLA, University of Amsterdam, Kruislaan 43, 198 SJ Amsterdam, The Netherlands email@example.com,More information
Deriving Business Intelligence from Unstructured Data
International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 9 (2013), pp. 971-976 International Research Publications House http://www. irphouse.com /ijict.htm DerivingMore information
Comparison of Implicit and Explicit Feedback from an Online Music Recommendation Service Gawesh Jawaheer email@example.com Martin Szomszor email@example.com Patty Kostkova firstname.lastname@example.com information
The «include» and «extend» Relationships in Use Case Models
The «include» and «extend» Relationships in Use Case Models Introduction UML defines three stereotypes of association between Use Cases, «include», «extend» and generalisation. For the most part, the popularMore information
Artificial Intelligence and Transactional Law: Automated M&A Due Diligence. By Ben Klaber
Artificial Intelligence and Transactional Law: Automated M&A Due Diligence By Ben Klaber Introduction Largely due to the pervasiveness of electronically stored information (ESI) and search and retrievalMore information
A Variability Viewpoint for Enterprise Software Systems
2012 Joint Working Conference on Software Architecture & 6th European Conference on Software Architecture A Variability Viewpoint for Enterprise Software Systems Matthias Galster University of Groningen,More information
Information access through information technology
Information access through information technology 1 Created to support an invited lecture at the International Conference MDGICT 2009 in Tamil Nadu, India, December 2009 by firstname.lastname@example.com information
Using Transactional Data from ERP Systems for Expert Finding
Using Transactional Data from ERP Systems for Expert Finding Lars K. Schunk 1 and Gao Cong 2 1 Dynaway A/S, Alfred Nobels Vej 21E, 9220 Aalborg Øst, Denmark 2 School of Computer Engineering, Nanyang TechnologicalMore information
Dissecting the Learning Behaviors in Hacker Forums
Dissecting the Learning Behaviors in Hacker Forums Alex Tsang Xiong Zhang Wei Thoo Yue Department of Information Systems, City University of Hong Kong, Hong Kong email@example.com, email@example.com,More information
Creating Usable Customer Intelligence from Social Media Data:
Creating Usable Customer Intelligence from Social Media Data: Network Analytics meets Text Mining Killian Thiel Tobias Kötter Dr. Michael Berthold Dr. Rosaria Silipo Phil Winters firstname.lastname@example.com information
Finding Advertising Keywords on Web Pages. Contextual Ads 101
Finding Advertising Keywords on Web Pages Scott Wen-tau Yih Joshua Goodman Microsoft Research Vitor R. Carvalho Carnegie Mellon University Contextual Ads 101 Publisher s website Digital Camera Review TheMore information
Approaches of Using a Word-Image Ontology and an Annotated Image Corpus as Intermedia for Cross-Language Image Retrieval
Approaches of Using a Word-Image Ontology and an Annotated Image Corpus as Intermedia for Cross-Language Image Retrieval Yih-Chen Chang and Hsin-Hsi Chen Department of Computer Science and InformationMore information
Digital libraries of the future and the role of libraries
Digital libraries of the future and the role of libraries Donatella Castelli ISTI-CNR, Pisa, Italy Abstract Purpose: To introduce the digital libraries of the future, their enabling technologies and theirMore information
Sentiment Analysis on Big Data
SPAN White Paper!? Sentiment Analysis on Big Data Machine Learning Approach Several sources on the web provide deep insight about people s opinions on the products and services of various companies. SocialMore information
Folksonomies versus Automatic Keyword Extraction: An Empirical Study
Folksonomies versus Automatic Keyword Extraction: An Empirical Study Hend S. Al-Khalifa and Hugh C. Davis Learning Technology Research Group, ECS, University of Southampton, Southampton, SO17 1BJ, UK {email@example.com information
1 CHAPTER 1 INTRODUCTION Exploration is a process of discovery. In the database exploration process, an analyst executes a sequence of transformations over a collection of data structures to discover usefulMore information
Chapter-1 : Introduction 1 CHAPTER - 1. Introduction
Chapter-1 : Introduction 1 CHAPTER - 1 Introduction This thesis presents design of a new Model of the Meta-Search Engine for getting optimized search results. The focus is on new dimension of internetMore information
Requirements. Approaches to user support. Command assistance. Chapter 11 User support
Chapter 11 User support Issues different types of support at different times implementation and presentation both important all need careful design Types of user support quick reference, task specificMore information
UMass at TREC 2008 Blog Distillation Task
UMass at TREC 2008 Blog Distillation Task Jangwon Seo and W. Bruce Croft Center for Intelligent Information Retrieval University of Massachusetts, Amherst Abstract This paper presents the work done forMore information
Michelle Light, University of California, Irvine EAD @ 10, August 31, 2008. The endangerment of trees
Michelle Light, University of California, Irvine EAD @ 10, August 31, 2008 The endangerment of trees Last year, when I was participating on a committee to redesign the Online Archive of California, manyMore information
Contact Recommendations from Aggegrated On-Line Activity
Contact Recommendations from Aggegrated On-Line Activity Abigail Gertner, Justin Richer, and Thomas Bartee The MITRE Corporation 202 Burlington Road, Bedford, MA 01730 {gertner,jricher,firstname.lastname@example.com information
, pp.-40-44. Available online at http://www. bioinfo. in/contents. php?id=42 SPATIAL DATA CLASSIFICATION AND DATA MINING RATHI J.B. * AND PATIL A.D. Department of Computer Science & Engineering, JawaharlalMore information
CiteSeer x in the Cloud
Published in the 2nd USENIX Workshop on Hot Topics in Cloud Computing 2010 CiteSeer x in the Cloud Pradeep B. Teregowda Pennsylvania State University C. Lee Giles Pennsylvania State University Bhuvan UrgaonkarMore information
Keywords the Most Important Item in SEO
This is one of several guides and instructionals that we ll be sending you through the course of our Management Service. Please read these instructionals so that you can better understand what you canMore information
CRISP - DM. Data Mining Process. Process Standardization. Why Should There be a Standard Process? Cross-Industry Standard Process for Data Mining
Mining Process CRISP - DM Cross-Industry Standard Process for Mining (CRISP-DM) European Community funded effort to develop framework for data mining tasks Goals: Cross-Industry Standard Process for MiningMore information
Automated Text Analytics. Testing Manual Processing against Automated Listening
Automated Text Analytics Testing Manual Processing against Automated Listening Contents Executive Summary... 3 Why Is Manual Analysis Inaccurate and Automated Text Analysis on Target?... 3 Text AnalyticsMore information
Natural Language Aided Visual Query Building for Complex Data Access
Proceedings of the Twenty-Second Innovative Applications of Artificial Intelligence Conference (IAAI-10) Natural Language Aided Visual Query Building for Complex Data Access Shimei Pan Michelle Zhou KeithMore information
David Chappell INTRODUCING AZURE SEARCH Sponsored by Microsoft Corporation Copyright 2015 Chappell & Associates Contents Understanding Azure Search... 3 What Azure Search Provides...3 What s Required toMore information
Texas Success Initiative (TSI) Assessment
Texas Success Initiative (TSI) Assessment Interpreting Your Score 1 Congratulations on taking the TSI Assessment! The TSI Assessment measures your strengths and weaknesses in mathematics and statistics,More information
MIRACLE at VideoCLEF 2008: Classification of Multilingual Speech Transcripts
MIRACLE at VideoCLEF 2008: Classification of Multilingual Speech Transcripts Julio Villena-Román 1,3, Sara Lana-Serrano 2,3 1 Universidad Carlos III de Madrid 2 Universidad Politécnica de Madrid 3 DAEDALUSMore information
Improving Web Page Retrieval using Search Context from Clicked Domain Names
Improving Web Page Retrieval using Search Context from Clicked Domain Names Rongmei Li School of Electrical, Mathematics, and Computer Science University of Twente P.O.Box 217, 7500 AE, Enschede, the NetherlandsMore information
Social Media Implementations
SEM Experience Analytics Social Media Implementations SEM Experience Analytics delivers real sentiment, meaning and trends within social media for many of the world s leading consumer brand companies.More information
How to Have a Successful School Library or Classroom Blog. By Karen Bonanno www.kb.com.au
How to Have a Successful School Library or Classroom Blog By Karen Bonanno How to Have a Successful School Library or Classroom Blog From: Karen Bonanno September 2014 Once you have chosen a topic or focusMore information
V3 Technical Editorial Services. For HL7 Contract Work Announcement V3 Technical Editor
1 1 1 1 1 1 1 1 0 1 0 1 V Technical Editorial Services For HL Contract Work Announcement V Technical Editor RIM Document Editorial Assessment June 00 Ockham Information Services LLC 0 Adams Street Decatur,More information
PDF hosted at the Radboud Repository of the Radboud University Nijmegen
PDF hosted at the Radboud Repository of the Radboud University Nijmegen The following full text is an author's version which may differ from the publisher's version. For additional information about thisMore information
Relevance Feedback versus Local Context Analysis as Term Suggestion Devices: Rutgers TREC 8 Interactive Track Experience
Relevance Feedback versus Local Context Analysis as Term Suggestion Devices: Rutgers TREC 8 Interactive Track Experience Abstract N.J. Belkin, C. Cool*, J. Head, J. Jeng, D. Kelly, S. Lin, L. Lobash, S.Y.More information
Mathematics Cognitive Domains Framework: TIMSS 2003 Developmental Project Fourth and Eighth Grades
Appendix A Mathematics Cognitive Domains Framework: TIMSS 2003 Developmental Project Fourth and Eighth Grades To respond correctly to TIMSS test items, students need to be familiar with the mathematicsMore information
TEMPER : A Temporal Relevance Feedback Method
TEMPER : A Temporal Relevance Feedback Method Mostafa Keikha, Shima Gerani and Fabio Crestani {mostafa.keikha, shima.gerani, email@example.com University of Lugano, Lugano, Switzerland Abstract. TheMore information
TREC 2007 ciqa Task: University of Maryland
TREC 2007 ciqa Task: University of Maryland Nitin Madnani, Jimmy Lin, and Bonnie Dorr University of Maryland College Park, Maryland, USA nmadnani,jimmylin,email@example.com 1 The ciqa Task InformationMore information
Tools to support the workflow Fernando Diaz, Sue Dumais, Jaap Kamps, Cathy Smith, Simone Stumpf, Elaine Toms, Arjen de Vries, and Maria Zemankova
TOOLS AND SUPPORT Tools to support the workflow Fernando Diaz, Sue Dumais, Jaap Kamps, Cathy Smith, Simone Stumpf, Elaine Toms, Arjen de Vries, and Maria Zemankova Problem The breakout group focused onMore information
2. Basic Relational Data Model
2. Basic Relational Data Model 2.1 Introduction Basic concepts of information models, their realisation in databases comprising data objects and object relationships, and their management by DBMS s thatMore information
Archetypal Analysis in Marketing Research: A New Way of Understanding Consumer Heterogeneity
Archetypal Analysis in Marketing Research: A New Way of Understanding Consumer Heterogeneity Dr. Paul Riedesel President, Action Marketing Research Introduction Archetypal analysis is a method for analyzingMore information
The Analysis of Online Communities using Interactive Content-based Social Networks
The Analysis of Online Communities using Interactive Content-based Social Networks Anatoliy Gruzd Graduate School of Library and Information Science, University of Illinois at Urbana- Champaign, email@example.com information
LogCLEF 2010: the CLEF 2010 Multilingual Logfile Analysis Track Overview
LogCLEF 2010: the CLEF 2010 Multilingual Logfile Analysis Track Overview Thomas Mandl 1, Giorgio Maria Di Nunzio 2, Julia Maria Schulz 1 1 Information Science, University of Hildesheim, Germany {mandl,email@example.com information
Towards SoMEST Combining Social Media Monitoring with Event Extraction and Timeline Analysis
Towards SoMEST Combining Social Media Monitoring with Event Extraction and Timeline Analysis Yue Dai, Ernest Arendarenko, Tuomo Kakkonen, Ding Liao School of Computing University of Eastern Finland {yvedai,More information


text:
|<< Previous book
|Next book >>
An Atlas of Love(2014)
An anthology of stories edited by Anuja Chauhan
An Atlas of Love, edited by celebrated bestselling author Anuja Chauhan, is an anthology of romantic shorts that daringly explores the many guises of romance, from its purest form to its darkest depths. 'Phoenix Mills' takes you through a young man's anguished quest for love; 'Post-Coital Cigarette' makes you flinch at a married man's interpretation of love; and 'Jilted' shows you that love can also be courageous. You will find yourself in the middle of a torrid liaison in 'The Affair', revel in the euphoria of budding romance in 'Just One Glance' and discover what it means to let go of your loved one in 'The Impasse'. Love can also be brutal and unconventional as 'The Unseen Boundaries of Love' and 'Something about Karen' will show you. But most of all, as 'Death of a Widower' and 'Siddharth' show, you will see that love is all about hope and taking the leap of faith. Selected from a nationwide Romance Contest conducted by Rupa Publications, this heart-warming collection of stories urges you to believe that love is eternal... and forever.
Similar Books by other authors...
Half Love Half Arranged
Itisha Peerbhoy
The Wedding Photographer
Sakshama Puri Dhariwal
Sita - Warrior of Mithila
(Ram Chandra, book 2)
Amish Tripathi
A Perfect Mismatch
Leena Varghese
Used availability for Anuja Chauhan's An Atlas of Love
See all available used copies of this book at Abebooks UK or Abebooks US
Paperback Editions
January 2014 : Paperback
|Title: An Atlas of Lover: The Rupa Romance Anthology
Author(s): Anuja Chauhan
Publisher: Rupa Publications
Availability: Amazon Amazon UK Amazon CA
Kindle Editions
January 2014 : USA, Canada, UK Kindle edition
|Title: An Atlas of Love
Author(s): Anuja Chauhan
Publisher: Rupa Publications India
Availability: Amazon Amazon UK Amazon CA
Genre Pages


text:
Who are we?
Glorious PC Gaming Race LLC was founded in 2014 by a gamer who was fed up with overpriced, and low-quality PC gaming peripherals/products. The company started through the Reddit community, where we built and designed products catered to the gaming communities suggestions and requests.
Here at Glorious PC Gaming Race LLC, we strive to deliver our customers the best quality PC gear at an affordable and reasonable price.
We are currently located in the USA, with our warehouse in Salt Lake City, Utah and headquarters, deep in the heart of Dallas, Texas. In 2015, we rapidly expanded to several European territories, and are working hard to bring our products locally to gamers worldwide.
We are located at:
Glorious PC Gaming Race LLC
2080 S. Industrial Rd, STE C
Salt Lake City, UT 84104
United States


text:
Couple's Initial Heart Necklace in Stainless Steel (2 Initials)
Item# 20007250
It's the two of you now � let everyone know with this personalized couple's necklace. Crafted in sleek stainless steel, this design carries the two initials you choose sculpted in a pretty script font and joined together by a perfectly filled heart. Polished to a brilliant shine, this personalized style suspends centered along an 18-inch rolo chain that secures with a lobster claw clasp. View product details
Typographic errors are subject to correction. Merchandise enlarged to show detail and may not always be exactly as pictured. Promotions, prices, styles and availability may vary by store and online.