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The present invention is directed generally to providing systems and methods for data analysis. More specifically, embodiments may provide system(s) and method(s) including dynamic user modeling techniques to capture the relational and dynamic patterns of information content and/or users' or entities' interests. Various embodiments may include system(s) and method(s) that are based on, for example, the past history of content semantics, temporal changes, and/or user community relationship. Various embodiments may include modeling and/or analysis of the dynamic nature of an item of interest's value to a user(s)/entity(ies) over time. The dynamic factors may be consider in any manner, such as, individually or combined, sequentially or simultaneously, etc. Further, some embodiments may include, for example, system(s) and method(s) relating to analyzing data to capture user/entity interests and/or characteristics, consider content semantics and evolutionary information, and/or using community relationships of users/entities to thereby analyze information and provide dynamic conclusion(s) (e.g., recommendation(s)).
1. A method of data analysis, comprising:
determining with a processor dynamic patterns over time of user characteristics;
determining with a processor dynamic patterns over time of information of interest to users; and
generating with a processor predictions or recommendations based on users' dynamic pattern variations and documents' dynamic patterns.
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10. A data analysis system, comprising:
a memory;
a processor configured to:
determine dynamic patterns over time of user characteristics;
determine dynamic patterns over time of information of interest to users; and
generate predictions or recommendations based on users' dynamic pattern variations and documents' dynamic patterns.
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This application claims the benefit of U.S. Provisional Application No. 60/738,825 filed Nov. 22, 2005, the entire disclosure of which is hereby incorporated by reference as if set forth fully herein.
This disclosure may contain information subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent disclosure or the patent as it appears in the U.S. Patent and Trademark Office files or records, but otherwise reserves all copyright rights whatsoever.
1. Field of the Invention
The present invention relates to the field of data mining and analysis and, more specifically, to methods and systems relating to utilizing content, dynamic patterns, and/or relationship information in data analysis.
2. Description of Related Art
Recent expansion of the availability and amount of information that people throughout the world are exposed to and have available to them is incredible. For example, information available on computer networks, e.g., intranets, the Internet, email, cellular networks, and other sources of digital media, have provided people access to an immense wealth of information. In fact, the amount of information available to people and organizations is starting to overwhelm people and organizations. Further, more people and organizations rely more-and-more on the Internet and various information networks to obtain information. As a result, it is desirable to have tools that help manage the massive amount of information available to people and organizations and to analyze and provide desired information or results from that analysis that is more focused, concise, and better suited for their needs. For example, it may be desirable to have method(s) and system(s) that may automatically predict, identify and/or recommend information (e.g., items, documents, etc.) that people or organization may find more or most useful. Therefore, a number or method(s) and system(s) have been developed that help to manage the massive amount of information available by, for example, analyzing the information available and providing recommendations.
Recommendation method(s) and system(s) have become a particularly important area since the appearance of the collaborative filtering in the mid 1990s. Examples of various applications for recommendation method(s) and system(s) include recommending books, CDs, and other products at, for example, Amazon.com, recommending movies by MovieLens, etc. There are also a number of methods and systems that provide personalized recommendations, content, and services to users. Some examples include those describe in U.S. Pat. Nos. 6,266,649, 6,912,505, and 6,853,982, and U.S. Patent Application No. US20050198056.
Content-based filtering, collaborative filtering, and hybrid approaches are three exemplary recommendation systems. Some examples of these approaches are described in D. Pierrakos, G. Paliouras, C. Papatheodorou, and C. Spyropoulos, Web usage mining as a tool for personalization: A survey. User Modeling and User-Adapted Interaction, 13:311-372, 2003; G. Adomavicius, and A. Tuzhilin, Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, IEEE Trans. Knowl. Data Eng. 17(6), 734-749, 2005; and M. Balabnovic and Y. Shoham, Content-Based, Collaborative Recommendation, Communications of the ACM, March 1997. Although these approaches give some reasonable results, there is room for improvement in the systems and methods of data analysis and particularly with respect to providing recommendations.
The prior systems and methods lack certain useful capabilities. For example, prior recommendation systems and methods typically do not consider the dynamic nature of both information (e.g., the change in value of information over time) and personal interests (e.g., changing interests of a user or entity). The importance of items and the interests of users are both naturally dynamic and change over time. Therefore, there is a need for recommendation systems and methods that can make recommendation(s) based on, for example, the dynamic nature of an item of interest and/or formal or informal communities to which user(s)/entity(ies) are a part of, or interested in.
The present invention is directed generally to providing system(s) and method(s) for data analysis. More specifically, embodiments may provide system(s) and method(s) including dynamic modeling techniques to capture the relational and dynamic patterns of information content and/or users' or entities' interests. Various embodiments may include system(s) and method(s) that are based on, for example, the past history of content semantics, temporal changes, and/or user community relationship. Various embodiments may include modeling and/or analysis of the dynamic nature of an item of interest's value to a user(s)/entity(ies) over time. Further, various embodiments may include, for example, system(s) and method(s) relating to analyzing data to capture the dynamic nature of user/entity interests, preferences, and/or characteristics. Various embodiments may use the dynamic aspects of items and user/entity interest to model user(s)/entity(ies) relationships, rank item(s), and/or providing various inferences and conclusion(s) (e.g., recommendation(s)) using formal and/or informal community relationships of users/entities. The invention may include data analysis system(s) and method(s) that include, for example, feature extraction, content analysis, dynamic pattern analysis, community construction, an adaptive user model, and/or a recommender. Various embodiments may include dynamic user modeling system(s) and method(s) or procedure(s) to capture the relational and dynamic patterns of users' interests based on, for example, the past history of content semantics, temporal changes, and/or user(s)/entity(ies) community relationship. Various embodiments may also include procedures that may represent both users' and items' (e.g., documents') dynamic patterns and may combine them both to provide various inferences and conclusions (e.g., recommendation(s)). The dynamic factors may be consider in any manner, such as, individually or combined, sequentially or simultaneously, etc. In various embodiments, the invention may be a computer implemented invention.
In various embodiments, the present invention is directed to providing a hybrid recommendation technique including collaborative filtering and content based filtering attributes in which, for example, both the dynamic aspects of item's changing value and user's interest may be considered in providing recommendations. More particularly, the present invention may include system(s) and method(s), referred to herein as Community Based Dynamic Recommendation (CBDR), which may exploit one or more factor(s), for example, an items' content semantics, an items' dynamic characteristics such as expiration times, users' characteristics such as intentions, users' evolving interests, and/or users' relationships and may be based on formal and/or informal communities. This recommendation approach may infer, for example, user interests, community structures, etc. The CBDR may be performed for, e.g., both informal communities, in which people with similar evolutionary interests may be grouped together, and/or formal communities, in which people may be grouped together using, for example, a formal relational chart and/or organization chart. Further, some embodiments may include, for example, a generative probabilistic model, referred to herein as a Content-Time-Community (CTC), to assess the dynamic aspects of users/entities, which may construct informal communities of people/user(s)/entity(ies) with similar evolutionary preferences and interests, in which the contextual, relational, and temporal aspects may be simultaneously considered. As such, various embodiments may use the community-based scheme and thereby avoid the cold-start problem of new users. In this respect, people from the same community may be provided with the same set of recommendations without need to include items (e.g., documents) that have been specifically retrieved by the people/user(s)/entity(ies). Various embodiments may utilize time sensitive adaptive modeling to assess the dynamic aspects of items that may rank items of interest based on user(s)/entity(ies) communities. This time-sensitive adaptive modeling may be, referred to herein as a Time-Sensitive Adaboost procedure, and may rank, for example, item (e.g., documents) contents based on leveraging dynamic factors, for example, freshness, popularity, item lifespan, expiration status, user intention, and/or other attributes. As one skilled in the art would recognize, the CTC and time-sensitive Adaboost techniques may be used independently or together, with this or other data analysis procedures. Further, one skilled in the art will recognize that the present invention may be applied to numerous applications in which a prediction, trend or recommendation is desired to be derived from information contained in a data set.
Still further aspects included for various embodiments will be apparent to one skilled in the art based on the study of the following disclosure and the accompanying drawings thereto.
The utility, objects, features and advantages of the invention will be readily appreciated and understood from consideration of the following detailed description of the embodiments of this invention, when taken with the accompanying drawings, in which same numbered elements are identical and:
The present invention is directed generally to providing systems and methods for data analysis. More specifically, the present invention may perform dynamic data analysis taking into consideration various data characteristics (e.g., items) and/or user/entity characteristics. For example, consideration of user/entity changing interest and/or an item's usefulness may prove more beneficial at predicting future interests and/or providing improved recommendations. Various embodiments of the present invention may model evolutionary and relational behaviors for community based dynamic recommendation. The invention may include data analysis system(s) and method(s) that include feature extraction, content analysis, dynamic pattern analysis, community construction, an adaptive user model, and/or a recommender. Various embodiments may include dynamic user modeling system(s) and method(s) or procedure(s) to capture the relational and dynamic patterns of users' interests based on, for example, the past history of content semantics, temporal changes, and/or user(s)/entity(ies) community relationship(s). Various embodiments may also include procedures that may represent both users'/entities' (e.g., individuals, organizations, etc.) and items' (e.g., documents, products, services, etc.) dynamic patterns and may combine them both to provide various inferences and conclusion(s) that may be used to generate, for example, predictions, recommendation(s), etc., derived from analyzing data from a data set.
Although the present invention has numerous applications, one exemplary application that will be used herein to describe the present invention is data analysis of information found in documents maintained in a data set, so as to provide a personalized recommendation to a user/entity as to which documents they should review. However, one skilled in the art will recognize that invention is also applicable to numerous other applications that may, or may not be, described or mentioned herein (e.g., stock price analysis and predictions). In any case, the system(s) and method(s) of the present invention described herein may exploit several dynamic factors, for example, document expiration times, users' intentions and/or users' evolving interests, which may be used to build models for recommending items to users/entities based on formal and/or informal communities.
Content-based filtering characterizes what a user likes, based on the past history of the user activity and the classification of the items. For example, a content-based book recommender system may use information extraction and machine learning techniques for text categorization. Another content-based filtering system may adaptively construct a user's model from the browsing behavior of the user, based on the similarity between Web documents containing news items. Content-based systems can recommend new items to users. However, they usually require a significant amount of time on user model training or depend on manually created user profiles. Furthermore, they are usually limited by the features that are explicitly associated with the items. If the features of two different items are literally the same, then they become indistinguishable because they contain, for example, the same words, even though these same words are not actually referring to the same matter or content. As a result, content-based systems will find it difficult to provide accurate recommendations for a new user having very few data access or rating history (e.g., a "cold start" problem).
Collaborative filtering approaches may infer a user's interests/preferences from that of the other people with similar tastes by considering what the user's is accessing or selecting and concluding that based on that selection the user should like what other people who previously accessed or selected the same thing may have also accessed or selected. A variety of collaborative filtering systems and algorithms have been developed to identify users of similar interests. Nearest Neighborhood, Bayesian Network, and Clustering are three major modeling methods. Nearest Neighborhood is the most typical method that computes similarity scores between all pairs of users, and the predictions for a given user are generated by weighting other users' ratings proportionally to their similarity to the user. A variety of similarity metrics have been tested, including correlation, mean-squared difference, or vector similarity. Bayesian Network methods construct off-line decision trees of user interests, and are suitable for static environments, but not frequently changing scenarios. Clustering methods identify groups of users who have similar interests. In order to make accurate recommendations, a collaborative filtering system must first learn users' preferences on items. The fact that new items must be chosen by some users before they can be recommended to other users may also create what is known as the 'cold start' problem. Recommendations are strongly biased toward what have been chosen in the past. This usually results in aggregating recommendations of just the most popular items.
Several recommendation systems use a hybrid approach by combining collaborative and content-based methods, which helps to avoid the above mentioned limitations of content-based and collaborative systems. The content-based and collaborative systems can be combined by including two separated recommenders, adding content-based characteristics to Collaborative Models, adding collaborative-based characteristics to Content Models, or building a unified model.
The importance of items and the interests of users are both naturally dynamic. However, this evolutionary property of both items and users had not previously been addressed by content-based, collaborative, and/or hybrid recommender systems and techniques. For instance, time may play an important role for collaborative filtering. People's tastes and organization's objectives typically change over time so that it is unlikely that data drawn from a long time ago will be useful in general for suggesting recommended items to the other users over time. On the other hand, users with a similar evolutionary trend on their tastes may find the data helpful in selecting valuable items for them. Some content-based approaches, have studied how to learn users' long-term and short-term interest categories in a dynamic environment. They have proposed a model to represent a user's interests, which maintains a long-term interest descriptor to capture the user's general interests and two short-term interest descriptors (a positive and a negative) to keep track of the user's more recent, faster-changing interests. In any case, these studies have not gone far enough to utilize the dynamic nature of a user's interest and an item's value.
The present invention may include, for example, a hybrid prediction/recommendation approach that may consider changes in both the value of an item or document content and/or user/entity interests, which is referred to herein as Community Based Dynamic Recommendation (CDBR). CDBR may provide predictions/recommendations based on formal community and/or informal community user/entity associations or relationship. A generative probabilistic model, referred to herein as a Content-Time-Community (CTC) model, may be used to construct informal communities of people/users/entities with similar evolutionary preferences and interests, in which the contextual, relational, and temporal aspects may be simultaneously considered. This community-based technique may be used to avoid the cold-start problem of new users/entity(ies) that may be experienced with a explicit personalized scheme (e.g., providing an inference, prediction, and/or recommendation may not be possible due to lack of data or information about a particular user(s)/entity(ies)). A means for assessing changes in user(s)/entity(ies) interest over time may be provided by what is referred to herein as a time-sensitive Adaboost procedure or process and may be used for ranking item (e.g., documents) contents based on leveraging dynamic factors such as freshness, popularity, item life-span, expiration status, user intention, and/or other attributes. Using these techniques, a community-based dynamic recommendation (CBDR) scheme may be used to infer various user interests as well as community structures. Of course, the various aspects mentioned above may be considered sequentially, individually or in any order.
Applying the particular application of the present invention to the particular application of providing recommendations, one problem solved is how recommendation system(s) and method(s) can adapt to the dynamic aspects of both the value of an item or content and the evolution of the user interest. Value of content and/or items may change over time. For example, with respect to information contained in documents, some documents are time-sensitive and may expire quickly with a life-time as short as several weeks (e.g., some news information). Some information may be timeless, for example documents such as classic technical papers may be of continuous interest for many people. Some people's/user's/entities' intentions may also be different. For example, some users tend to log into various information databases (e.g., the Internet) to get update information, so they may mostly access and read relatively recently added information (e.g., the latest news related information). Others log in to various information databases to look for specific information, and they may tend to refer to long-term documents. Also, people's interests also evolve over time or may be sporadic. One person, who is interested in a certain topic one year ago, may not care about the same topic any more or at a later time, and changes their interests to something else at least for a period of time, or forever. Thus, the present invention may consider, for example, information content semantics, evolutionary information, and user/entity community relationships and in various particular applications (e.g., providing recommendations) use them to analyze the dynamic aspects of items of interest and user(s)/entity(ies).
Various embodiments of the present invention may take these components and apply them to, for example, model and predict peoples' intentions and/or classify the semantic types and evolutionary characteristics of items. In recently developed data analysis methods and systems, user models may be represented as relational and evolutionary graphs. These properties may be incorporated together for predicting the human behavior of receiving and disseminating information. In the present invention, this approach has been expanded upon by the "Content-Time-Community" (CTC) model which has been developed for constructing, for example, the informal communities of people with similar evolving preferences and interests. Further, the "time-sensitive Adaboost" procedure has also be developed to build adaptive user models that will be, for example, used for ranking items such as documents in a community-based recommendation system. Based on these new approaches, the Community-Based Dynamic Recommendation (CBDR) scheme has been developed for building a system that may be applied to, for example, dynamic database document recommendation applications.
Various embodiments of the present invention may provide a novel dynamic user modeling method to capture the relational and dynamic patterns of users'/entities' interests based on the past history of, for example, content semantics, temporal changes, and user community relationship, and may do so simultaneously. This modeling methodology may solve the cold-start and/or concept drift problems. In particular embodiments, the system(s) and method(s) of the present invention may explicitly represent both users' and documents' dynamic patterns, and may use them for making inferences, predictions and/or conclusions (e.g., providing personalized recommendations)
To tackle the new-user 'cold-start' problem, recommendations may be conducted at the community level. People/users/entities from the same community may be provided with the same set of recommendations, and this set may exclude the documents that have been retrieved by the specific user/entity to which a recommendation is to be given. In various embodiments, both the formal communities, which may be constructed by using, for example, an organizational chart, and/or informal community, in which people with similar evolutionary interests are grouped together, may be used. As such, a new user/entity may be assigned to an existing community profiles in order to avoid the cold-start problem.
One objective of the invention may be to push or provide relevant information to user(s)/entity(ies) at the right time. To achieve this objective, the invention may focus on dynamic adaptation of the value of content or an item of interest to user(s)/entity(ies) and the evolution of a user's/entity's needs or interest. Personalized dynamic recommendation may require a determination of dynamic patterns in both user's behaviors and documents' characteristics, as well as an understanding of how they affect personalized recommendation. Thus, the invention may find evolving communities and content topics, and rank items adaptively based on the changing dynamics.
In at least one embodiment direct to providing personalized document recommendations, relational and dynamic patterns may be explored for clustering people into communities with similar dynamic interests. Features, for example a bag of words from the abstracts and titles of the documents, may be extracted from a data set to describe, for example, the content of the documents, and the freshness, popularity, authors of the documents. User models from both community level and individual level may be provided to combine the contextual, relational and temporal information. The time-sensitive Adaboost may be used to help rank the various documents to selected recommendations.
Referring now to
The feature extractor/extraction 110 may be coupled to a content analyzer/analysis 115, a dynamic pattern analyzer/analysis 120 and a community constructor/construction 125. The content analyzer/analysis 115 may, for example, provide the content analysis for determining whether there is similarity between various items in the data set. For example, the content analyzer/analysis 115 may determine if there is word similarity, topic similarity, etc. between various items in the data set. The dynamic pattern analyzer/analysis 120 may, for example, provide the analysis for determining such things as the popularity of the data or information, the freshness of the data or information (e.g., is the information less relevant now), the expiration status of the data or information (e.g., is the data or information expired or outdated), the anticipated life-span of the data or information, changes in user/entity actions and/or intentions, etc. The community constructor/construction 125 may, for example, determine various relationships between users/entities. This may include any formal association or informal association between users/entities. The content analyzer/analysis 115, dynamic pattern analyzer/analysis 120 and/or community constructor/construction 125 may be coupled to an adaptive user model 130. The adaptive user model 130 may be, for example, a time sensitive model that considers variation in information over time to estimate a user'/entity's dynamically changing interest and/or an item's (of interest) change in value. The adaptive user model 130 may be coupled to a recommender 135. The recommender 135 may provide various insights, suggestions, conclusions, and/or recommendations about item(s) to the user/entity based upon the nature of the information, the characteristics of the user, and the dynamic aspects of, for example, the users/entity interests and/or item's change in value.
Referring to
The content analyzer/analysis 230 may, for example, provide the content analysis for determining whether there is similarity between various items in the data set. For example, the content analyzer/analysis 230 may determine if there is word similarity, topic similarity, etc. between various items in the data set. The dynamic pattern analyzer/analysis 235 may, for example, provide the analysis for determining such things as the popularity of the data or information, the freshness of the data or information (e.g., is the information less relevant now), the expiration status of the data or information (e.g., is the data or information expired or outdated), the anticipated life-span of the data or information, changes in user/entity actions and/or intentions, etc. Dynamic factors from both users and documents point of view may be analyzed. For example, according to how long people will be interested in them, items, information and/or documents may be classified into two types: short-term and long-term. Then, users' intentions, for example, getting updated news or looking for specific information may be detected based on the types of information and/or documents they download. Furthermore, the expiration dates of items, information, and/or documents may be predicted so as to retire or remove some items, information, and/or documents from the recommendation candidate list (as will be described in more detail later).
The community constructor/construction 240 may, for example, determine various relationships between users/entities. This may include any formal association or informal association between users/entities. The community constructor/construction 240 may consider both formal and informal communities. A formal community may be constructed based on, for example, an organization chart, which is simply based on the fact that people from the same organization, company, or department of the same company, etc., may tend to have similar interests, since they may have similar subject matter or educational backgrounds and/or may be working on similar projects or objectives. In various embodiments, the community constructor/construction 240 may include a Content-Time-Community (CTC) model, which is a generative probabilistic model that will be described in more detail below, that may be used to construct informal communities based on, for example, people's preference and similarity of interests.
The content analyzer/analysis 230, dynamic pattern analyzer/analysis 235 and/or community constructor/construction 240 may be coupled to a time-sensitive Adaboost 250. As described and explained in more detail below, the time-sensitive Adaboost may be a stage-wise estimation procedure that incorporates the evolutionary changes of the user/entity interest and the value of the an item over time. In any case, a ranked recommender for items 260 may be coupled to the time-sensitive Adaboost 250, so as to provide a ranked list of items (e.g., products, documents, services, etc.) to be recommended to a user/entity.
Referring to
Content analysis 415 may include word similarity analysis 416, Latent Dirichlet Allocation (LDA) analysis 417, and topic similarity analysis 418. With respect to content analysis 415, a problem may occur when assessing similarity of documents based on raw word count in each document. Unfortunately in the English language, words may be synonyms and thus have the same meaning even though they are different words, and words may be polysems and take on different meanings for different uses or contexts. This distinction must be determined and overcome by the word similarity analysis 416. With respect to topic similarity analysis 418, there may be topics that based on their description may not appear to be similar, but they are. This distinction must be determined and overcome. With respect to LDA, 417, each document may have a probability distribution over topics and each topic may have a probability distribution over words. The LDA may be used to more accurately reflect the sorting of various documents into topics by producing mixture components and mixture weights. These content analysis aspects of the invention will be described in more detail below.
Dynamic pattern analysis 425 may include, for example, popularity 426, expiration status 427, item life-span 428, freshness 429, and user intention 430. Item popularity 426 may indicate how many times one item is accessed during a time period. Expiration status, 427 may indicate whether a particular item is more or less likely to have lost its value. This is a gage to reflect the fact that in various situations, the longer an item is not accessed by anyone, the less likely it is that the item will be accessed in the future; so, it may have expired and be no longer of any value in predicting the future. The item life-span 428 pattern analysis may be used to identify items that may be of short-term interest (e.g., announcements, conference notes, etc.) and those that may be of long term interest (e.g., text books, seminal technical articles, reference manuals, etc.). Freshness 429 may be an assessment and utilization of the difference between a user's/entity's log in or data access time and the item's initial disclosure or publication time. Finally, user intention 430 detection may be an assessment of whether the user/entity is typically more interested in short term type of information or long-term type of information. These dynamic pattern analysis aspects of the invention will be described in more detail below.
Community construction 435 may include both formal communities 436 and informal communities 438. The formal communities 436 may be a construction of users/entities formal relationships and may be derived from organizational charts 437, membership lists, identification of job positions, etc. The informal communities may be derived by clustering people/users/entities together who may have similar evolving needs or interests. One means of doing this may be use of the Content-Time-Community (CTC) model 439. The CTC model 439 may use the facts that each user is a probability distribution over various communities at a point in time, each community is a probability distribution over topics at a point in time, and each topic is a probability distribution over words. These probabilities may be nested to give a time dependent assessment of various user's/entity's inclusion in various informal communities or groups. These community construction aspects of the invention will be described in more detail below.
Any and all of the features generated by the content analysis 415, dynamic pattern analysis 425, and community construction 435 may be provided to a time-sensitive Adaboost 445. The time-sensitive Adaboost 445 may use adaptive user models 446 at both an individual user/entity level 447 and/or a community level 448. For example, the time-sensitive Adaboost 445 may assign larger weight to more recent data and smaller weight to older data to indicate the different data's relative importance. The time-sensitive Adaboost 445 will be described in more detail below.
A filtering/prediction 455 may receive the output of the time-sensitive Adaboost 445, and use it for various applications 456. For example, the system(s) and method(s) may be applied to a data set(s) to provide a personalized recommendation 457. One example may be the personalized recommendation to a user/entity of various documents identified or maintained in the data set. Another example may be providing a prediction of what a user/entity will like 458. For example, a user/entity may like a particular product or service that is being offered for sale or is available that has been identified by the information contained within the data set 405. As noted above, numerous applications of the invention are possible and a prediction may be provided for any number of subject matters.
Referring to
To better understand the operation of the dynamic pattern analysis of the present invention, it will be helpful to discuss some of the various characteristics of the analysis with the assistance of test data. So, a dataset collected from NEC's "EigyoRyoku 21" (denoted as ER and stands for Sales-Force in Japanese) system is selected as a representative data set. The ER system is a knowledgebase to support the NEC sales staff with registered documents. Users can search, preview, download, edit or provide feedbacks on the documents in the system. A set of recommended documents may be manually selected for all users. A one-year period of click stream logs was collected from April 2004 to March 2005 covering over 30,000 users and over 20,000 documents. Included in the dataset are users' demographic information, and the authors, titles, abstracts, and disclosure timestamps of the documents. Nine user actions are identified: {"Login", "Register_Feedback", "Preview", "Abstract", "Document Download", "Search", "Register", "Update", "Delete"}. The click stream log may be partitioned into sessions that start as "login" and then represent a collection of ordered sequences of user actions and may become part of log files. The documents in the system are heterogeneous, e.g., including short-term documents such as announcements, and long-term documents such as technical manuals. For testing purposes herein, it may be assume that if a user downloads a document, he or she is interested in that document.
Applying a dynamic pattern analysis to this data set, it may be explored what dynamic patterns exist in both user's behaviors and documents, and understand how they affect the personalized recommendations. Consider the document access patterns. First, all the available documents from the ER database between April 2004 and March 2005 are collected, and the expected useful life-time of each document in the ER database is estimated. The life-time of a document may be defined, for example, as the time period when 90% of the downloading behavior occurred. However, the data set may need to be cleaned up a bit so that the results are not skewed from including irrelevant or erroneous data (e.g., step 515). So, first of all, all weekends and holidays may be removed from the database. In total, 241 days were left as the valid working days. People's/user's self-downloading behavior and system administrator's downloading behaviors are regarded as noise and also removed from the statistics.
Based on this data, a number of studies were performed. In one study, two types of downloading patterns were found to exist in the data set. These downloading patterns may give indications of an item (e.g., document) life-span. The life-span histograms 600 and 650 of two documents' life-span are illustrated in
Turning now to document expiration date prediction, various methods may be used to predict when various documents have expired. Based on observation, it was concluded that one criteria may be that the longer no person/user/entity downloads a document, the less likely it is that the document may be downloaded in the future. Since this is a closed set (the maximum number of people accessing the documents is fixed), the more the people have already accessed a document, the less likely it will be accessed again. Noisy-or model from Bayesian Networks may be used in this case as one possible means to calculate the probability of a binary event with multiple possible causes. In the present invention, it may be used to estimate the probability of when a document will expire. In the noisy-or model, it may be assumed that an event can only happen if at least one of the causes occurred. It may also be assumed that the probability of any cause failing to trigger the event is independent of any other causes. Thus, the probability of one document being expired may be determined by:
Referring to
Now a discussion of various user dynamics that may be derived from the ER test data. Users' dynamics may include many aspects: they may come and go from the system, their intentions may be different from each other, their preferences and interests may also change over time, etc. Here the test data helps show an example of how to capture users' intentions and the interest evolution.
People's intention of using the system can be different: some users/entities only access the data set for updating their knowledge of various information, then they will only read what's new i.e., mostly interested in "short-term" documents; some users/entities only access the data set for details of various informative materials, then they will search, look at abstracts and preview the documents, and finally download the documents i.e., more interested in "long-term" documents. If users'/entities' intention is known or can be predicted before they log into the system and access the data set, then recommendations may be improved by fitting their needs of, for example, short-term or long-term documents.
Based on the ER test data, the types of documents users touched or down-loaded, the probability of how much they like short-term documents as well long-term documents may be estimated. Referring to
Referring now to
It may be assumed that a user's/entity's preferences/interests do not typically change suddenly. Although the user/entity may not access the documents of the same topic continuously, the user's interests/preferences on the documents of one topic during one time period may keep contributing to his choices in the near future. This goal may be achieved by smoothing the evolutionary user matrices along the time axis. The invention may apply a sliding window with width C along the time axis to collect the preference for each unit in the evolutionary preference matrix. C could be by days, by months, or by half a year, etc.
A forgetting function may also be applied to the user's/entity's preferences/interests. A user's/entity's recent preferences/interests may be more important than their preferences of a long time ago, particularly for the user's/entity's current choices, selections, or needs. Thus, a forgetting function may be applied to the dynamic preferences matrix:
Now an explanation of the content analysis process will be provided. Referring now to
Referring now to
Also, the same word in the English language may have multiple meanings (i.e., polysems). For example, the word "bank" may be used to refer to a place that manages money or the land that abuts to the water in a lake, river or ocean. As illustrated, document 3 (1225) may include the words "bank" and "money." A document 4 (1230) may include the words "bank" and "river." In this case, document 3 and document 4 both contain the word "bank" but these documents do not likely have any similarity because the word "bank" has a different meaning in document 3 and document 4, and thus they should not be related to one another. However, based on a simple word count or matching, document 3 and document 4 might be considered related. Therefore, the conceptual dimension must be considered to properly relate documents having the same or similar topics instead of just words matching.
Referring now to
Referring now to
At step 1430, data content and time may be analyzed to determine an informal community construction. Informal communities may be constructed from people with similar interests or preferences. Based on the ER test data set used herein, each person may have a set of documents he touched of downloaded, at the same time, a document includes a set of terms. As a result, the relationship may be comprised of a three-level relationship: people-document-term. A three-way aspect model may be used to tackle the three-way co-occurrence data among users, documents, and document content. In addition, the variation of these terms over time leads to a more complex analysis. Thus, in various embodiments, a Content-Time-Community (CTC) model may be used for constructing informal communities based on people's evolving interests and preferences. In any case, at step 1440 time and community information is provided. At step 1450 the process stops.
Referring to
Referring now to
Assume that documents may be represented as random mixtures over latent topics, where a topic may be characterized by a distribution over words; people may be represented as random mixtures over latent groups, where a group may be characterized by a distribution over topics that are downloaded by this group of people. Then, given T topics and K groups of people, the probability of the i-th word in a given document appearing in time ti may be formalized as:
By using, for example, a Gibbs Sampling process (see, e.g., T. Griffiths and M. Steyvers, Finding Scientific Topics, Proc. of the Natil. Academy of Sciences, 5228-5235, 2004) we may construct a Markov chain that converges to the posterior distribution on z and g, and then use the results to infer φ, θ, and φ.
Now we will turn to the construction of an adaptive user model that takes output from the content analyzer/analysis, dynamic pattern analyzer/analysis, and/or the community constructor/construction and helps determine what items (e.g., documents) user(s)/entity(ies) will be interested in knowing about. Referring to
In the following few paragraphs, the proposed Community-Based Dynamic Recommendation (CBDR) system(s) and method(s) will be described, including its components, how to infer user implicit preference, how to extract the features for representing users' interests and preferences, and how to fuse these features for discerning user interests using, for example, the time-sensitive Adaboost. Referring now to
As previously noted, the analysis components may include feature extraction, for example, static feature extraction and/or dynamic feature extraction, which may be used in developing the community-based dynamic models. Further, the dynamic pattern analysis 1825 may also determine item expiration status 1830.
In the testing phase 1845, based on the community-based dynamic models 1855 and available documents 1850 in which expired documents are excluded, a rank list of documents 1865 (or items) may be provided. The rank list of documents may be inferred based on how likely one document is interesting to one group of users (e.g., a community). This may be achieved by using, for example, the Time-Sensitive Adaboost procedure, and the top-ranked documents may be recommended to the user(s)/entity(ies).
As previously noted, for formal community-based recommendation, Latent Dirichlet Allocation (LDA) may be used for content analysis. Given T topics, the probability of the i-th word in a given document may be formalized as:
From the accessing patterns of documents, people's implicit interests on documents may be inferred; i.e., implicit user preference inference. If a user accesses one document, it may be inferred that this user is interested in this document. However, it may be difficult to infer which documents he or she does not like since it is not known what kind of documents he or she has seen in the data set but has decided to skip. On the other hand, the assumption may be made that if a document was accessed often by many people, and at that period, this user did not access it, he may not be interested in this document.
Identification of various features of the data information and user information may be useful in structuring the CBDR. Instead of relying on only content or collaborative aspect from what user(s)/entity(ies) may like, the system(s) and method(s) of the present invention may gain insight into a user's/entity's preferences about why they like particular items from both static and dynamic aspects. Some static features may include content aspects, author's organization, collaborative aspects, user title, etc. With respect to content aspect, P(z|d), the probability of a topic given a document, may be considered as a content feature, and may estimated as
Some dynamic features may include, for example, freshness of the data, item, or information (e.g., documents), life-span of data, item, or information (e.g., short-term or long-term type document), popularity of the data, item, or information (e.g., document), and/or user intention. With respect to freshness of, for example, documents, every document may have a life-time period of value. Even for a very famous paper, its usefulness will still decay over time. The freshness of a document may be represented as the difference between user's login time and the disclosure time of the document. With respect to life-span, for example the short-term or long-term type of the document, the probability of how likely one document belongs to the short-term type may be set as the feature of the document's type. With respect to popularity of the data, item, information, in the case of documents, similar to the popularity of the authors, the popularity of documents may also be taken into consideration, represented by how many times one document was downloaded by others during a time period. With respect to user intention, the probability of how likely one user will access the data, item, information, for example download short-term documents, may be set as a feature for the user intention.
Although various static and dynamic feature have been presented herein with respect to applying the present invention to document recommendation, one skilled in the art would recognize that numerous variations in the particular static and dynamic features that may be considered will be driven by the particular data set to which the invention is being applied and the particular desired result (e.g. providing future prediction, recommendation, etc.) one wishes to achieve. Therefore, the present invention is not limited to the particular examples or applications provided herein, and particularly to the various static and dynamic features that may be included or considered in generating an improved outcome.
In any case, give the identification of the various features, these may be applied to, for example, a recommendation model. Given a particular set of features representing, for example, one document, the recommendation model P(c=1|x,t) may represents what kind of documents one user may be interested in during one time period, where c=1 or 0, respectively, indicating whether the user may be interested or not in this documents, where x may represent the features for judgment and t may represent a time stamp for a particular user login. As previously noted, a time-sensitive Adaboost method may be used to incorporate the evolutionary interest changes of a user' entity. The Adaboost algorithm may be a stage-wise estimation procedure for fitting an additive logistic regression model by minimizing a loss function. The simple rules may be trained sequentially; each rule may be trained on examples which are most difficult to classify by the preceding rules. One exemplary approach may be using a Real Adaboost procedure which finds an
Observing more recent behaviors may be more useful for predicting a user's/entity's current behaviors, a time-sensitive Adaboost model may be useful at addressing this problem. Recall that for Adaboost, the goal may be to minimize
Referring now to
Instead of asking users/entities to manually rate recommendations, the community-based dynamic recommendation procedure may be evaluated by an off-line method based on the user log files from the ER system. The dataset may be divided into two sets: a training set and a testing set. The data from April 2004 to February 2005 may serve as the training data, and the data from the month of March 2005 may serve as the test data. In the original ER system, recommended documents are manually selected for all users to read. For evaluation purposes, five documents may be recommended for each user/entity that logged into the ER system in March 2005. The goal is to measure whether the recommended documents match the documents that the user really downloaded. Users' satisfaction may be calculated by measuring how many people really downloaded the documents among these five recommendations during the test period.
Using the community-based dynamic recommendation system(s) and method(s) of the present invention, the same list of documents may be recommended to people in the same community, where each person may be provided five documents that he or she has never downloaded. Terminology used in the evaluation is defined as follows.
"Community-based recommendation usefulness metric" may be defined as how many people downloads at least one, two, three, four, or five documents from the recommendation list during the test time period. Since it may be assumed that if a user downloads a document, he or she may be interested in this document, this metric measures how many documents out of the recommendation list this user is interested in.
"Global upper bound" may be define as the best performance a system can achieve in terms of global recommendation, if it already knows which items the system users will download during the testing period. "Global recommendation" defines that the same items are recommended to all users in the system. Given the system logs of the testing data, it can be found that the best items that maximize a usefulness metric by brute-force approaches. "Global upper bound" is the maximum usefulness metric that any recommendation system can achieve.
"Community upper bound" may be defined as the best performance a system can achieve in terms of community-based recommendation, if it already knows which items the users of a specific community will download during the testing period. "Community-based recommendation" defines that the same items are recommended to all users in a community. Given the system logs of the testing data, the best items can be found that maximize a usefulness metric by brute-force approaches. "Community upper bound" is the maximum usefulness metric that any recommendation system can achieve given the community clusters.
Referring now to
In the experiments that follow, 10% of the data was held out for test purposes and the models were trained on the remaining 90% of the data. In order to exclude casual users who had very few activities in the system, active users who downloaded at least 20 documents in the whole year may be selected, 10 documents in 2005, and 5 documents in March 2005 for experiments. As a result, 697 active users from 202 formal communities are selected based on this criterion.
To better understand the advantages of the informal community community-based dynamic recommendations compared to the formal community community-based dynamic recommendations, each formal community may be studies and compare the performance if each of its members is recommended by his formal community or informal community.
Referring now to
Referring to
As shown by these experimental results, the CDBR technique provides improved prediction and/or recommendation results than what may be obtained using conventional approaches. Taking the dynamic aspects of items and users into consideration results in improved results. The experimental results on the ER on-line application system demonstrate the recommendation usefulness of the formal CBDR scheme is 106% better than the global recommendation upper bound, 259% better than the collaborative filtering, and 126% better than the static recommendation algorithm. Furthermore, the informal CBDR method also shows 13% improvement over the formal CBDR method.
As noted, in various embodiments, the system(s) and method(s) provided herein may be implemented using a computing device, for example, a personal computer, a server, a mini-mainframe computer, and/or a mainframe computer, etc., programmed to execute a sequence of instructions that configure the computer to perform operations as described herein. In various embodiments, the computing device may be, for example, a personal computer available from any number of commercial manufacturers such as, for example, Dell Computer of Austin, Tex., running, for example, the Windows™ XP™ and Linux operating systems, and having a standard set of peripheral devices (e.g., keyboard, mouse, display, printer).
Instructions may be read into a main memory from another computer-readable medium, such as a storage device. The term "computer-readable medium" as used herein may refer to any medium that participates in providing instructions to the processing unit 2605 for execution. Such a medium may take many forms, including, but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media may include, for example, optical or magnetic disks, thumb or jump drives, and storage devices. Volatile media may include dynamic memory such as a main memory or cache memory. Transmission media may include coaxial cable, copper wire, and fiber optics, including the connections that comprise the bus 2650. Transmission media may also take the form of acoustic or light waves, such as those generated during Radio Frequency (RF) and Infrared (IR) data communications. Common forms of computer-readable media include, for example, floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, Universal Serial Bus (USB) memory stick™, a CD-ROM, DVD, any other optical medium, a RAM, a ROM, a PROM, an EPROM, a Flash EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read.
Various forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to the processing unit(s) 2605 for execution. For example, the instructions may be initially borne on a magnetic disk of a remote computer(s) 2685 (e.g., a server, a PC, a mainframe, etc.). The remote computer(s) 2685 may load the instructions into its dynamic memory and send the instructions over a one or more network interface(s) 2680 using, for example, a telephone line connected to a modem, which may be an analog, digital, DSL or cable modem. The network may be, for example, the Internet, and Intranet, a peer-to-peer network, etc. The computing device 2600 may send messages and receive data, including program code(s), through a network of other computer(s) via the communications interface 2610, which may be coupled through network interface(s) 2680. A server may transmit a requested code for an application program through the Internet for a downloaded application. The received code may be executed by the processing unit(s) 2605 as it is received, and/or stored in a storage device 2615 or other non-volatile storage 2655 for later execution. In this manner, the computing device 2600 may obtain an application code in the form of a carrier wave.
The present system(s) and method(s) may reside on a single computing device or platform 2600, or on multiple computing devices 2600, or different applications may reside on separate computing devices 2600. Application executable instructions/APIs 2640 and operating system instructions 2635 may be loaded into one or more allocated code segments of computing device 2600 volatile memory for runtime execution. In one embodiment, computing device 2600 may include system memory 2655, such as 512 MB of volatile memory and 80 GB of nonvolatile memory storage. In at least one embodiment, software portions of the present invention system(s) and method(s) may be implemented using, for example, C programming language source code instructions. Other embodiments are possible.
Application executable instructions/APIs 2640 may include one or more application program interfaces (APIs). The system(s) and method(s) of the present invention may use APIs 2640 for inter-process communication and to request and return inter-application function calls. For example, an API may be provided in conjunction with a database 2665 in order to facilitate the development of, for example, SQL scripts useful to cause the database to perform particular data storage or retrieval operations in accordance with the instructions specified in the script(s). In general, APIs may be used to facilitate development of application programs which are programmed to accomplish some of the functions described herein.
The communications interface(s) 2610 may provide the computing device 2600 the capability to transmit and receive information over the Internet, including but not limited to electronic mail, HTML or XML pages, and file transfer capabilities. To this end, the communications interface 2610 may further include a web browser such as, but not limited to, Microsoft Internet Explorer™ provided by Microsoft Corporation. The user interface(s) 2620 may include a computer terminal display, keyboard, and mouse device. One or more Graphical User Interfaces (GUIs) also may be included to provide for display and manipulation of data contained in interactive HTML or XML pages.
Referring now to
To conclude, the present invention may be implemented on the computer system 2600 and/or the computer network 2700. As such, various embodiments of the present invention may provide an adaptive user-centric modeling technology that investigates contextual, relational, and temporal information, and may do so simultaneously. Dynamic factors from both documents and users, including documents of long-term or short-term interests, document expiration times, users' intentions, and users' evolving interests, may be exploited. In various embodiments, a community-based dynamic recommendation scheme may be used, in which people from the same community will be provided with the same recommendation of items so that new users may be assigned a community profile and benefit from the experience of other users. In various embodiments, a Content-Time-Community (CTC) model may be applied for finding people of similar interests to build evolutionary informal communities. Further, an adaptive user model, for example, a time-sensitive Adaboost procedure may be used for ranking various items (e.g., documents) based on leveraging features including, for example, static aspects, freshness, popularity, and other attributes for inferring dynamic user interests and/or community information.
While embodiments of the invention have been described above, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. In general, embodiments may relate to the automation of these and other business processes in which analysis of data is performed. Accordingly, the embodiments of the invention, as set forth above, are intended to be illustrative, and should not be construed as limitations on the scope of the invention. Various changes may be made without departing from the spirit and scope of the invention. Accordingly, the scope of the present invention should be determined not by the embodiments illustrated above, but by the claims appended hereto and their legal equivalents
All publications, patents, and patent applications cited herein are hereby incorporated by reference in their entirety for all purposes.
Patent Citations
Non-Patent Citations
Referenced by
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text: Hi, can the widget be added to a single page, and can widths be set by percentage? I.e. I want 4 columns on one row, but, I want them to be of different widths (I want the fourth column to be a lot wider than the other three, thanks, Earl

subset: commoncrawl


text:
Name: James Leslie Dickey
Location: St. Petersburg, Florida
Age: 71 years
Processing Date: 10-01-2009
An arrest does not mean that the individual has been convicted of the alleged violation. Individuals on this website are innocent until proven guilty by a court of law. All information related to charges and arrest or booking is obtained through public domain and in accordance with the Freedom of Information Act.
Ever wonder who gets arrested in Pinellas County, FL for Alcohol Related Offenses?
Here's how 67,164 Alcohol Arrests are distributed by age in Pinellas County, FL:


text:
In this edition of the Social Order Magazine, we take you to the drama that unfolded on the Knesset floor prior to the close of the winter legislative session. Bibi's coalition government brought 3 controversial bills in a package deal for votes. The bills, in the order they were voted upon, are the Governability Law, the Referendum Law, and the Haredi Draft Bill. In a rare procedural move, the coalition limited time for discussion. In response, a unified opposition boycotted the votes.
The magazine begins by discussing the Governability Law, which raises the electoral threshold and adversely affects the ability of Arab Parties to secure seats in future Knessets. Israel Social TV asks, how are the Arab parties preparing for their political futures?
The second bill is the Referedum Law, which requires a popular vote in order to cede Israeli territory under a peace agreement.
Next we look at the Haredi Draft Law, which eliminates a religious exemption for the Ultra-Orthodox from army service and replaces it with jail time for refusers. But what about secular Israeli citizens who refuse army service for ideological opposition to violence or occupation?
To conclude, we travel to Rabin Square. Last weekend, a group of Israeli activists organized a revival of the summer 2011 J14 Social Justice Protests. Did Israelis take to the streets to demonstrate for social and economic change?
Find answers to these questions in the third edition of the Israel Social TV Social Order Magazine.
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text:
A new motto: Get active, get healed
FORT BLISS, Texas — A group of Fort Bliss soldiers who are suffering the physical and emotional scars from war and constant training are on a new mission: to get active and heal by competing in sports.
Seven members of the Fort Bliss Warrior Transition Battalion will compete in the inaugural Southwest Regional Valor Games in San Antonio on Sept. 23-25.
The games are an opportunity for wounded, injured and ill service members and veterans to compete against each other in six sports – archery, cycling, kayaking, powerlifting, wheelchair basketball and air guns.
Being involved in sports absolutely can help with their recovery, said Marc Cattapan, military adaptive sports coordinator for the Fort Bliss Warrior Transition Battalion, which provides care for about 400 soldiers.
"The No. 1 side effect when a soldier gets wounded or injured is they get depressed and suffer a lack of motivation," Cattapan said. "Any activity can be adapted to any injury. We want soldiers to be aware that they can compete in any activity and that it can help them heal.
"Our unofficial motto is: Get active and get healed."
Chief Warrant Officer 2 Kimberly Lamberton of Long Beach, Calif., is a former helicopter pilot who survived two helicopters colliding and crashing in Afghanistan. Remarkably, everyone survived, but she's suffered from post traumatic stress and depression since then, she said.
She will compete in kayaking and cycling at the Valor Games, which are being put on by the Department of Veterans Affairs and U.S. Paralympics.
"Competition is nerve-racking but it's good to push yourself and test your limits," Lamberton said. "It's a challenge to see what I'm made of. A year ago, I wouldn't be doing this."
Spc. Isaac Brennan of Phoenix will compete in all six events at the games.
He injured his knees, hips and shoulders during repeated training exercises while stationed in Germany. He also started to suffer from depression after getting injured, he said.
"This has given me new hope, and I've regained physical confidence in myself," Brennan said.
The Valor Games are a notch below the Warrior Games, which are held every May in Colorado Springs Colo., at the Olympic Training Center, Cattapan said.
"It's an opportunity for soldiers who aren't top echelon athletes to compete and medal," Cattapan said. Organizers are expecting at least 125 athletes will compete.
Chief Warrant Officer 2 Will Harper is helping to train Fort Bliss' cyclists for the Valor Games.
"A lot of these guys can't go back to active duty because of their injuries and are transitioning to civilian life," Harper said. "This gives them a sense of accomplishment and gives them a new mission, which is to recover and reintegrate into society."
Sgt. Gary Saleh of Liberty, N.C., suffered a traumatic brain injury and burns during a training accident at the National Training Center at Fort Irwin, Calif. He also suffers from anxiety, depression and post-traumatic stress disorder from a tour in Iraq in 2006-07.
He will compete in archery, air guns, cycling and kayaking in San Antonio.
"It think of it as an opportunity to better myself," he said.
Saleh also hopes he can serve as a positive role model to other soldiers who have suffered injuries and send a message that they can be stay active despite their injuries.
Spc. Jorge Maldonado, of Murrieta, Calif., injured his back while doing training before his unit was going to deploy. He needs back surgery, but is competing in cycling, archery and air guns.
"When you are injured, you don't feel quite the same," Maldonado said. "This helps you physically and mentally."


text:
Early in 1991, while Operation Desert Storm (also known as the First Gulf War) was underway, I found myself on a call-in radio show in Salt Lake City, talking about Islam and the Middle East. Toward the very end of the program, a male caller asked what I thought of the idea of dropping nuclear bombs on the Iraqi cities of Basra and Baghdad. I was a bit taken aback by the question, but answered that I could see no valid reason for doing so. American troops were, after all, advancing against remarkably little serious resistance, like a hot knife through melted butter. Basra and Baghdad weren't really involved in the war, and obliterating them would serve no significant military objective. (We were simply trying to remove Iraqi occupation troops from Kuwait.) Finally, and much more importantly, I said, the pointless vaporizing of potentially millions of innocent civilians — men, women, and children — would, to put it extremely mildly, make it difficult for the United States to claim the moral high ground. Not only in that conflict, but ever. A president who ordered such an attack for absolutely no legitimate military purpose would immediately take his just place in the ranks of mass murdering modern tyrants such as Hitler, Stalin, Mao, and Pol Pot. And, I said, I would personally be unable to remain a loyal citizen of such a country. There was a very brief pause. "It's spineless liberals like you," the man screamed into the phone, "who cost us the war in Vietnam!" He slammed his phone down, and the program ended a minute or two thereafter.
I couldn't wait until the next day, to tell some of my genuinely liberal faculty colleagues that I'd been called a "spineless liberal" for having moral reservations about killing several million unarmed civilians for no real reason.
Now, fast forward:
My blog entry, yesterday, on the Islamic aspect of the Boston Marathon bombings and related events, has gotten an extraordinarily energetic response.
And some of it — particularly on Facebook, but also in a number of private emails — has been deeply disheartening.
One small but vocal contingent has pronounced all Muslims "animals" and is calling for their deportation from the United States. Not just militant extremist Muslims. All of them. Not simply illegal immigrants or even holders of green cards. All of them. Including American citizens. Not merely those born overseas. All of them. Even those who might be second- or third-generation citizens of the United States. Even those who have served, and sometimes been officers, in our military. (I suppose, though I haven't asked, that the deportation would extend even to sitting U.S. congressmen who might be Muslims. [There's at least one of them, right now.])
Imagine the sheer logistics of it: Estimates of the Muslim population in the United States are all over the map, but it probably ranges somewhere between 2.5 million and 5 million. Imagine rounding them all up for deportation. Would they first have to wear an identifying badge, something perhaps on the pattern of the yellow star with Jude written in it that was favored by a certain former chancellor of Germany and his government? Would there be internment camps? (They would have to be enormous.) What if these Muslims didn't want to go? Would the military need to be deployed? Would there be orders to shoot any who resisted? Would ordinary citizens perhaps be deputized and given license to kill?
One of the people advocating such deportation purports to be a member, as I am, of the Church of Jesus Christ of Latter-day Saints. I'm ashamed that this might actually be true.
I pointed out that the leaders of our church do not — I know, as a matter of direct personal knowledge, that they do not — share his hostility to Islam and to Muslims. This has had no discernible impact on him. He reminds me, in fact, of an old fellow who somehow got my cell phone number several years ago, and used to call me on it. His opening line was always "You're a liar." And then he would denounce me — typically until I hung up on him, which began to happen sooner and sooner — for my friendliness toward "that mass murdering, pedophile false prophet, Muhammad" and toward his evil modern disciples, the Muslims, who, the old fellow said, should be run out of our country. He boasted that, unlike me, he supported the Lord's true prophets, the modern apostles and leaders of our church. Once, I pointed out that, unlike him, those modern apostles and prophets had actually taken a pretty positive view of Muhammad, and I specifically cited the official statement issued by President Spencer W. Kimball and his counselors, N. Eldon Tanner and Marion G. Romney, in 1978. "President Kimball was deceived by the devil," he replied. (So much, I guess, for his claim that, unlike me, he supported the Lord's true prophets, the modern apostles and leaders of our church.)
Anyway, back to the present: In a further attempt to reason with this most recent fellow, I pointed out that it seemed really, really weird, to me, that a Mormon, of all things, would support the expulsion of an entire people from the United States solely on account of their religious faith. Did he not remember our own history, during which our forebears fled New York for Ohio, Ohio for Missouri, Missouri for Illinois, Illinois for the desert Great Basin in the West, and, in some cases, that same Great Basin for Canada and Mexico? Did he not remember, specifically, the notorious 1838 "extermination order" issued by Missouri Governor Lilburn W. Boggs against the Mormons?
His response has been that I myself should "go to Saudi Arabia." I am, he says, a "closet liberal."
To which my reaction is that, if the United States were ever to become the kind of fascist country that would expel millions of Muslims, not for any crime, but solely because they're Muslims, I could not remain a loyal American. (How, exactly, would such a country be morally superior to Saudi Arabia?) Indeed, I doubt that I would be permitted to do so, because the same sorts of people, by and large, who would expel the Muslims from our borders would very likely soon afterward turn their attention to other unpopular religious minorities. And we Latter-day Saints would, surely, rank high on that list. And any of our number who had shamefully endorsed the expulsion of innocent Muslims would have no moral standing whatever to resist such expulsion when the bigots turned toward them. In the end, the only representatives of real Americanism left might be the Aryan Brotherhood.
It's impossible not to be reminded, in this context, of a statement made by the German Protestant pastor Martin Niemöller (1892-1984), who spent the last seven years of the Second World War in Nazi concentration camps:
First they came for the Socialists, and I did not speak out–
because I was not a Socialist.
Then they came for the Trade Unionists, and I did not speak out–
because I was not a Trade Unionist.
Then they came for the Jews, and I did not speak out–
because I was not a Jew.
Then they came for me–and there was no one left to speak for me.


text:
Joe Rooney
Joe Rooney (born 1963) is an Irish actor and comedian from Tuam, co. Galway. His best-known acting role at home and abroad is that of Father Damo in the Channel 4 sitcom Father Ted. He features in one episode, series two's "The Old Grey Whistle Theft". More recently, Rooney has had a starring role in the RTÉ television comedy, Killinaskully, in which he plays Timmy Higgins.
Other acting roles include Fergus Scully, a roving reporter who speaks in pidgin English and Irish, on the TG4 sketch show Rí Rá. In 1997, he appeared with Paul Tylak in Messrs Tylak and Rooney, a twelve-episode TV3 comedy travel series.[1]
Joe Rooney will also be appearing in the movie "On the Ball" as a main character called "Deco Quinn" alongside actor Des Bird, which will be released in 2014.
Joe also appeared with Des in a promotional video called "End in Tears" also being released 2014
- ^ Friends For Life – Joe Rooney and Paul Tylak Sunday Tribune, 2 April 2006
External links[edit]
|This article about a comedian or humorist is a stub. You can help Wikipedia by expanding it.


text:
CHICAGO, June 04, 2007 /PRNewswire-FirstCall/ -- Pharmion Corporation today released interim findings from its Phase II, multi-center clinical study of Amrubicin, the company's third-generation synthetic anthracycline currently in development for the treatment of small cell lung cancer. These findings indicate favorable interim results in terms of response rate and safety when comparing Amrubicin to the current treatment standard, topotecan, in second-line treatment of platinum-sensitive small-cell lung cancer patients with extensive disease (ED-SCLC). The early results of this study were published online on the ASCO website during the 43rd Annual Meeting of the American Society of Clinical Oncology (ASCO) in Chicago, Ill. (June 1- 5, 2007).
"Oncologists have very limited treatment options available for patients with extensive-disease small cell lung cancer, and regrettably, patients with this disease still have an exceptionally poor prognosis," said Andrew R. Allen, chief medical officer of Pharmion. "We are encouraged by these initial results which suggest Amrubicin's potential to improve the treatment landscape for small cell lung cancer."
The trial compares Amrubicin and topotecan in patients with ED-SCLC that initially responded to first-line platinum-based therapy but recurred or progressed at least 90 days after completion of first-line treatment. Study participants are randomized 2:1 to receive either Amrubicin (40 mg/m2 via 5- min.infusion daily x 3 days) or topotecan (1.5 mg/m2 via 30-min. infusion daily x 5 days), both starting on Day 1 of a 21-day cycle.
Response data from 24 evaluable patients have been analyzed, 15
treated with Amrubicin and nine with topotecan. Of the 15 Amrubicin
patients, 40 percent showed an objective tumor response, including
two complete r


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This activity uses an episode of poor air quality over the Midwest and Eastern seaboard to engage students in an exploration of the collection, comparison, analysis and utilization of air quality data. With the aid of a camera, the EPA website and... (View More) Google Earth, students will determine local visibility. Students will also interpret images taken with the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard both the Terra and Aqua satellites to distinguish between three aerosols-smoke, dust and haze. Then students will then find online satellite images of the local area to determine aerosol types and possible sources. Four lesson extensions are included. This lesson uses the 5E instructional model. (View Less)
This guide provides instructions on how to make color pictures using images downloaded from the WISE archive. An overview of what digital images and FITS files are is followed by a discussion on the basic process of how gray scale images are... (View More) adjusted, assigned color, and added together to create a color picture. Five different image processing programs are provided to the users, along with the pros and cons of each program, and links for installing and using each one. There is also a review of the various image artifacts that may be found in WISE images. (View Less)
This Hubble Space Telescope image shows an especially photogenic group of interacting galaxies called Arp 273. The larger of the spiral galaxies, also known as UGC 1810, has a disk that is distorted into a rose-like shape by the gravitational tidal... (View More) pull of the companion galaxy below it, also known as UGC 1813. The encounter between the two galaxies also has created regions of intense star formation. In the accompanying educational activity, In Search of ... Interacting Galaxies, students investigate galaxy interactions through a level 1 inquiry activity using the images and text from the lithograph and other resources. A level 1 inquiry activity can help prepare students to become independent thinkers. (View Less)
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This website provides instructions on how to find and download images directly from the WISE archive. With a computer and Internet connection, users can access high-resolution, infrared images from the WISE mission. Instructions are provided to... (View More) explain how to get access the images from the archive. (View Less)
This Hubble Space Telescope image shows what appears to be a delicate bubble of gas floating serenely in space. In actuality, the bubble is the visible remnant of a powerful supernova explosion called SNR 0509. The bubble was formed from gas being... (View More) swept up by the expanding shock wave. In the accompanying educational activity, In Search of ... Supernova Remnants, students investigate supernova explosions and remnants through a level 1 inquiry activity using the images and text from the lithograph and other resources. A level 1 inquiry activity can help prepare students to become independent thinkers. (View Less)
This Hubble Space Telescope image captures thousands of sparkling young stars nestled within the star-forming nebula NGC 3603. This stellar "jewel box" is one of the most massive young star clusters in the Milky Way Galaxy. The image and text... (View More) describe a family portrait of stars in different stages of their lives, from infant stars still wrapped in cocoons of gas to stars nearing the end of their lives. In the accompanying educational activity, In Search of ... Stellar Evolution, students investigate stellar evolution through a level 1 inquiry activity using the images and text from the lithograph and other resources. A level 1 inquiry activity can help prepare students to become independent thinkers. (View Less)
These directions allow students to open different images that have been collected from the ROSAT satellite. The directions instruct the user to open the website, but first the user will need to install Hera software. (See related and supplementary... (View More) resources for a link to download Hera software at http://imagine.gsfc.nasa.gov/teachers/hera/install/install_hera.html). (View Less)
This poster includes an energy budget diagram on the front depicting our best understanding of energy flows into and away from the Earth. It is based on the work of many scientists over more than 100 years, with the most recent measurements from... (View More) NASA'S Clouds and the Earth's Radiant Energy System (CERES) satellite instrument, which provide high accuracy data of Earth's reflected solar and emitted infrared radiation fluxes. (View Less)
This is a lesson about the value of exploring our solar system and others in the Universe. Learners will investigate, compare, and describe patterns in Solar System data. They will then hypothesize about the formation of the Solar System based on... (View More) data and explain how extrasolar planets can be discovered. In the first activity, the students investigate Solar System data to find clues to how our planetary system was formed. By the end of the activity, the students come to understand that other stars form just like the Sun, and, therefore, many stars could have planets around them. The second activity examines how scientists can find these extrasolar planets. By observing the behavior of a model star-planet system, the students come to understand that it is possible to see the effect a planet has on its parent star even if the planet cannot be seen directly. By comparing the properties of our Solar System with other planetary systems, we can gain a deeper understanding of planetary systems across the Universe. (View Less)
Using a set of activities, recommendations, and diagrams, participants will construct a fully functional Space Weather Action Center (SWAC) for use in a classroom. Students will access, analyze, and record NASA satellite and observatory data to... (View More) monitor the progress of an entire solar storm. Afterward, they will transform the data collected in their student journals into real SWAC news reports using an adaptable SWAC script. (View Less)


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Smart lighting; visibility in low-light conditions at an affordable price. ½ Watt rear LED light for being seen at night.
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Light came promptly and in good order. Light was as described, mounted easily, and is working fine. I'd recommend the product and the site.
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1876 20C MS67 PCGS Secure. CAC....
2011 November 8-10 US Coins Signature Auction - Beverly Hills #1161
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Heritage Auctions - Beverly Hills
9478 West Olympic Blvd., 1st Floor
Beverly Hills, CA 90212
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Superb Gem 1876 Twenty Cent1876 20C MS67 PCGS Secure. CAC. The twenty cent piece was born out of Free Silver movement, an important political lobby that perceived the gold standard as a threat to poorer citizens, particularly farmers and silver miners. In the early 1870s many of the world's preeminent powers, including Germany, France, and the Latin Monetary Union, adopted the gold standard and either suspended or demonetized silver coinage.
Tied for Finest Certified
Tied for Finest Certified
The Mint Act of February 12, 1873, deemed the Crime of '73 by the silver interests, discontinued the silver dollar (as well as the two cent piece, silver three cent piece, and the half dime) and reduced the weights of the remaining silver coins to further widen the ratio between gold and silver. This was the de facto adoption of the gold standard by the United States. The actions by the U.S. and other countries, combined with the discovery of massive silver deposits in Nevada during the prior decade, greatly diminished the value of silver.
The Free Silver movement argued for the free coinage of silver: unlimited production of silver coins at a ratio that maintained their value relative to gold. This inflationary policy would benefit debtors, many of whom were farmers, because it would dramatically lower the real amount that would have to be paid back to the banks (inflation makes preexisting debts cheaper), and would be an obvious boon to the silver mine owners.
The Act of March 3, 1875 was the first piece of legislation passed by the silver interests and it authorized production of a silver twenty cent piece. Although the case was made that there was a need for small denomination coins in the West, the twenty cent piece was simply unnecessary because the dime and quarter already filled that void. In the first year of the denomination more than 1 million pieces were struck; the following year, 1876, fewer than 25,000 circulation strikes were produced. Only 14,640 of these coins were struck at the Philadelphia Mint.
On the other hand, in 1876 the dime and quarter each had mintages of more than 30 million pieces, easily supplying enough small change for the rural states. The twenty cent piece was simply redundant and production of circulation issues ceased in 1876 after only two years. (Proofs were manufactured for another two years before the denomination was completely abandoned.)
From a lowly mintage of just 14,640 pieces, this MS67 specimen is a remarkable survivor. It is tied with only two other examples for the status of finest certified at PCGS, and NGC does not report any examples higher (9/11). Attractive shades of blue and lilac cover both sides. The design definition is razor-sharp with the only softness observed on a couple of stars. As one would expect from the grade the surfaces are virtually immaculate. Flashy luster throughout the fields adds to the eye appeal.(Registry values: N7079) (NGC ID# 23R8, PCGS# 5299)
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Men's Specialty Chronograph Gold Textured Dial 18k Gold Plated SS INVICTA-14392 Watch
designer's notes
The Invicta makes a bold statement with its intricate detail and design, personifying a gallant structure. It's the fine art of making timepieces.
Gold textured
Case: 18k gold plated stainless steel
Case Back: Snap-down stainless steel
Case Diameter: 45 mm
Case Shape: Round
Case Thickness: 12 mm
Bezel: Black with silver numerals
Series: Specialty
Style: Chronograph
Dial Color: Gold textured
Hands: Black and luminous hour and minute. Black second
Markers: Black and luminous indices and Arabic numerals
Calendar: Date display at 6:00
Crown: Push/Pull
Subdials: 60 second, 60 minute and 24 hour
Crystal: Flame-Fusion
Material: Stainless steel
Water Resistant: 5 ATM/50 meters/165 feet
Movement: Japanese chronograph quartz
Bracelet Color: Gold
Bracelet Length: Up to 9.75 inches
Bracelet Material: 18k gold plated stainless steel
Bracelet Width: 20 mm
Clasp Type: Deployment with safety
Warranty information available at


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Tiger Woods shoots even-par 70, barely makes Honda Classic cut
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Tiger Woods made a plethora of birdies on Friday, but he cancelled those out with two bogeys and a double bogey and barely held his head above the weekend cut line.
Woods finished the round and the day at even par, which was the cutline going into the Saturday and Sunday rounds. He will tee off at 8:15 a.m. ET on Saturday.
He made a sweet sand save on No. 17, then hit his drive in the bunker on No. 18 but got out and made par to likely save himself from joining Rory McIlroy for another weekend match at the Medalist Club (Woods' home course) after McIlroy withdrew earlier Friday.
Woods actually found himself in a similar position at the Honda Classic last year, when he trailed Tom Gillis and Justin Rose by seven strokes after two rounds.
In that scenario, he shot a 69-62 in the weekend rounds to finish second. He'll need an even better back-to-back performance this weekend. He's nine back of this year's leader,
Here is Woods's colorful scorecard:
And here's video of one of the aforementioned birdies.
And if you missed it, here's Serena Williams getting busted for trying to take a photo of Tiger during his round on Friday.
For more golf news, rumors and analysis, follow @EyeOnGolf and @KylePorterCBS on Twitter and like us on Facebook.