www.cw-usa.com/coffee-international-delight-hazelnut-creamer-cup.html 
http://www.roundeyesupply.com/Nestle-Coffee-Mate-Regular-Liquid-Creamer-Portion-p/de356761.htm

A:

At least one maker calls them singles – short for single-serving, I presume.  
That said, in that case, I'd say single refers to the name of the product, not the container.  However, I might call it a single-sized creamer, or a disposable creamer.
As for a generic name of the container itself, I'm not sure if there's a formal name for a cup that small. In the case where they are filled with cream, flavored cream, or half-and-half, creamer cup could work.  Typing that term into Google images returns several variants:

However, if a company were to put, say, soy sauce in the same container, I don't think creamer cup would be apt for that.

 Q:

Evaluate performance of Recommendation engine

I have designed item-based recommendation engine with jaccard similarity measure and I incorporated time as well. See the example below:
prod | PR4  | PR5  | time
-------------------------
PR1  | 0.30 | 0.60 | 1
-------------------------
PR2  | 0.60 | 0.50 | 0.3
-------------------------
dot  | 0.48 | 0.45 | - 

On the vertical axis are products that user has, on the horizontal axis those which the user doesn't have. The time indicates when customer bought the product (so PR1 has been bought this month, PR2 3 months before - we assume that products closer to present are more important). And of course, the measure of similarity.
Furthermore, dot product between PR4, PR5 and time is calculated to estimate the best product (this is simplified, there are more steps).
So in this case, I'd offer to this customer product PR4.
Now the struggle I have - How to evaluate the performance of the recommendation engine. Often, (R)MSE is used, but there is no ground truth. And if I remove some products from customer and then observe whether the engine recommends them or not, the prediction will be skewed
www.cw-usa.com/coffee-international-delight-hazelnut-creamer-cup.html 
 http://www.roundeyesupply.com/Nestle-Coffee-Mate-Regular-Liquid-Creamer-Portion-p/de356761.htm 

 A: 

 At least one maker calls them singles – light for individual - serving, I presume.  
 That said, in that case, I'd allege single refers to the name of the product, not the container.   However, I might address it a unmarried - sized creamer, or a disposable creamer. 
 As for a generic name of the container itself, I'm not sure if there's a formal name for a cup that little. In the case where they are filled with cream, season cream, or half - and - half, creamer cup could work.   Typing that term into Google images returns respective variants: 

 However, if a company were to put, say, soy sauce sauce in the same container, I don't think creamer cup would be apt for that. 

  Q: 

 Evaluate operation of Recommendation engine 

 I have design item - based recommendation engine with jaccard similarity measurement and I incorporated time as well. See the example below: 
 prod | PR4   | PR5   | meter 
 ------------------------- 
 PR1   | 0.30 | 0.60 | 1 
 ------------------------- 
 PR2   | 0.60 | 0.50 | 0.3 
 ------------------------- 
 dot   | 0.48 | 0.45 | - 

 On the vertical axis are products that user has, on the horizontal axis those which the user doesn't have. The time bespeak when customer bought the product (so PR1 has been buy this calendar month, PR2 3 months before - we assume that products closer to present are more important). And of course, the measuring stick of similarity. 
 Furthermore, dot product between PR4, PR5 and time is calculated to calculate the best product (this is simplified, there are more steps). 
 So in this case, I'd volunteer to this customer product PR4. 
 Now the struggle I have - How to evaluate the operation of the recommendation engine. Often, (R)MSE is used, but there is no ground truth. And if I remove some products from customer and then observe whether the locomotive recommends them or not, the prediction will be skewed
www.fw-usa.com/coffee-internatiokal-delight-hazelnot-xreamec-cup.htmm 
http://wwd.roundeyesupply.com/Nestle-Coffxe-Mare-Regylar-Liquid-Creamer-Portiun-p/de356761.htm

A:

Wt least one naker calls them slugles – shoxt for single-serying, I presgme.  
That said, iv chat case, I'd say single refers to thq name pf the product, njt tnq cohnalner.  However, I might call it a aingle-spzed creamer, or a disposable creamer.
As for w gejeric name of the fontainer ijaels, I'm not sure if there's a formal naje for a cup that small. In the zase chere they qrw flnled with cceam, fjavored cream, or halx-and-halg, creamer cup gould woek.  Typing that term mnto Google images rgturns sevarcl variants:

However, if a compdny fere ro out, sey, aoy sakce in the saje containee, I don't think creakew cup would be apt fjr that.

 Q:

Evaluate performance of Recommengatjon engine

I have designwd item-based recommenfation endine with jaccard similarity measure and I incorpmratev gimt qs weuo. Dee the example below:
prod | PR4  | PR5  | time
-------------------------
PR1  | 0.30 | 0.60 | 1
-------------------------
IR2  | 0.60 | 0.50 | 0.3
-------------------------
dot  | 0.48 | 0.45 | - 

On the vrrhivwl axis are ptoducts thzt user has, on the horizogtal qxis thost whivh the user doesn't have. The time indicanes qhen customer bougkt the produet (so KR1 has been bought this month, PR2 3 months beflre - we aarume that producgs blosar to prtrent are more impjrtant). Anv of eourse, tfe mgasure jf similarlty.
Fuvdhermore, dot produft bejween [R4, PR5 and hime is calculated to estimate vie best produvt (thps is sim'lifiec, there are mjre steps).
So in this ccse, I'd offer to nhis custmmer produce PR4.
Now the sdtuggle I have - How to evaouatw the pdfformance of tne recommvnbation entine. Often, (R)MSE is usgd, but there is ni gtiund truth. And if I welote soia products fsom zusgpmer xnd then obwdrve whether the engine secojmends them or not, tme predicjion will be skewed
www.cw-usa.com/coffee-international-delight-hazelnut-creamer-cup.html http://www.roundeyesupply.com/Nestle-Coffee-Mate-Regular-Liquid-Creamer-Portion-p/de356761.htm A: At least one maker singles short for I presume. That say refers to the of the product, the container. However, I might call a single-sized creamer, or a disposable creamer. As for a generic name of container itself, I'm not sure if there's a formal name for a cup small. the where are filled with cream, flavored cream, or half-and-half, creamer cup could work. Typing that term into images returns several variants: However, if a company to put, say, soy in the same container, I think cup would apt that. Evaluate performance of engine I have designed item-based recommendation engine with jaccard similarity measure and I incorporated time as well. the example | PR4 PR5 time PR1 | 0.30 | 1 ------------------------- PR2 | 0.60 0.3 ------------------------- dot | 0.48 | 0.45 | On the axis are products that user has, the horizontal axis those which the user doesn't The time indicates when customer bought the product (so PR1 has been bought this month, months before - we that products closer present more And course, the of similarity. Furthermore, dot product between PR4, PR5 and time is to estimate the best product (this is simplified, there are So this case, I'd to this customer product Now struggle I have - evaluate performance engine. (R)MSE used, but there is ground truth. And if I some products from customer engine recommends them or not, the prediction will skewed
www.cw-usa.com/coffee-internatIonal-deligHt-hazElnUt-cReAmer-Cup.hTml 
http://www.rounDEyesUpply.com/Nestle-Coffee-MaTe-RegUlAR-LiqUId-creamEr-PortiON-p/DE356761.Htm

a:

AT lEasT oNE mAker cAllS them siNgles – short For SiNgle-serving, I PReSume.  
That saId, iN that case, I'd sAy sIngle rEfErs TO the nAme Of the ProducT, Not the Container.  hoWEver, I mIGht call IT A sInglE-sized creamer, or a dISpOSable creamer.
As For a geNeRIc NAMe oF thE container ItSelf, I'M Not sure IF tHERE's a FOrmal name for a Cup that smalL. in tHe case WhEre THey are FilleD wITh cReam, flavoreD creAm, or half-aNd-half, CReamer cUP could wOrk.  TypIng ThaT terM InTo gooGlE ImaGEs RetURns Several vArIaNts:

HoWeveR, IF A CompAny Were To put, Say, soy sauce in The Same COntAiner, i don't ThinK cReameR cup woUld be ApT for that.

 Q:

EvaluaTe peRformance Of REcOmmEnDatioN Engine

i haVe dEsigned Item-basED reCoMMENdAtion engine with jacCaRD SiMilarity MeasurE AnD I INcorporaTeD tiMe as WELl. See The eXAmPle below:
Prod | PR4  | pr5  | tImE
-------------------------
PR1  | 0.30 | 0.60 | 1
-------------------------
PR2  | 0.60 | 0.50 | 0.3
-------------------------
doT  | 0.48 | 0.45 | - 

ON the veRtIcaL axIs are PRoduCts thaT user has, On the HOrizontal axis tHOse which the usER dOESn'T Have. the Time indicatEs whEN cusTomeR BoUghT The prOduct (So pr1 hAS been bought this montH, Pr2 3 monthS befoRe - we assume thaT products cLOSEr to presEnt aRE mORe important). And Of couRse, the measURe of simiLaritY.
FurtherMore, dot prODUct betweEn Pr4, PR5 And TimE IS cAlculated to esTIMate ThE best prOduCt (this iS siMplIfiEd, tHeRe are more Steps).
So iN tHiS cAsE, I'd Offer TO this cusToMer PrOduCt PR4.
NOW the stRugglE I haVe - hoW To eValuate THe PERforMaNcE of tHe rEcOmmenDatiON enGine. OftEn, (R)MSE is uSed, BUt thErE iS no grouNd truth. And if I ReMove some prOdUctS from cUSTomer and Then observe whether the enGIne recoMmeNds thEm or Not, the preDicTion wiLl bE Skewed
www.cw-usa.com/coffee-inte rnational- delig ht- haz el nut- crea mer-cup.html h ttp: //www.roundeyesupply.c om/Ne st l e-Co f fe e-Mat e-Regul a r- L i qui d- Cr eam er - Po rtion -p/ de35676 1.htm

A:
At  l east one mak e rcalls them  si ngles – shor t f or sin gl e-s e rving , I  pres ume. That s aid, in t ha t  case, I'd say s in glerefers to the nam e  o f  the product,not th ec on t a ine r. However,Imight call it as i n gle - sized creamer , or a disp o sab le cre am er. As for  a ge ne r icname of the  con tainer it self,I 'm nots ure ifthere' s a  fo rmal na me  fo ra  cu p  t hat sma ll. In t he  c ase w here t h e y ar e f ille d wit h cream, flav ore d cr e am,  or h alf-a nd-h al f, cr eamercup c ou ld work.  Typin g th at term i nto  G oog le  imag e s retu rns  se veral v ariants : 

H ow e v e r,  if a company were  t o pu t, say,soy sa u ce  i n  the sam econ tain e r , I d on't th ink crea mer cu p  w ou ld be a pt  for t ha t.
 Q :

Ev a luat e perf ormanceof Re c ommendation en g ine

I have d e si g n ed item -ba sed recomme ndat i on e ngin e  w ith jacca rd si mi l ar i ty measure and I in co rporat ed ti me as well. S ee the exa m p l e below: 
pro d  | PR4  | PR5  |time---------- - -------- ----- -
PR1  |  0.30 | 0 . 6 0 | 1
-- --- --- --- --- - - -- -------
PR2 | 0.60  |  0.50 |  0. 3
----- --- --- --- --- -- ------
do t  | 0.4 8|0. 45  |- 

O n  the ver ti cal  a xis  arep roduct s tha t us er  h a s,on theh or i z onta lax is t hos ewhich  the use r doesn 't have.The time  i nd icateswhen customer  b ought thepr odu ct (so P R1 has b een bought this month,P R2 3 mo nth s bef ore- we assu methat p rod u cts cl oser t o pre se nta r e mor e im por ta nt). And o f cou rse,th e me asure o f similarity.
Furt h erm ore, dot prod uct  bet w e en  PR 4 ,P R5an d  ti m e  is calculatedto estimat et he  best prod u ct(t his issimplif ied,t here ar e more st eps).
Soin  thi s cas e, I'd off er to th is custom e r pro d uc t PR4 .
N ow the  s tru ggleI have - H ow to  evalu at e theperfo rm ance ofthe recommendation engi ne. Of ten,(R) MSE is us ed, but  there is  noground tru th.  An d ifI r e movesome pr odu c ts fr om c u stomer an d  t hen o bs erve whethe r t heengin e r e commen ds t hem or not, the p r ediction willbe s k e wed 
www.cw-usa.com/coffee-international-delight-hazelnut-creamer-cup.html 
http://www.roundeyesupply.com/Nestle-Coffee-Mate-Regular-Liquid-Creamer-Portion-p/de356761.htm

A:

At_least one_maker calls them singles_– short_for_single-serving, I_presume._ 
That said,_in that case,_I'd say single refers_to the name_of_the product, not the container.  However, I might call it a single-sized creamer,_or_a disposable_creamer.
As_for_a generic name of the_container itself, I'm not sure_if there's_a formal name for a cup that small._In_the case where_they are filled with cream, flavored cream, or half-and-half,_creamer cup could work.  Typing_that term into_Google_images_returns several variants:

However, if_a company were to put, say,_soy sauce in the same container,_I don't think creamer cup would be_apt for that.

 Q:

Evaluate performance of_Recommendation engine

I have designed item-based_recommendation engine_with jaccard similarity measure and_I incorporated time_as well._See the example_below:
prod | PR4  | PR5_ | time
-------------------------
PR1_ | 0.30 | 0.60 |_1
-------------------------
PR2_ | 0.60_|_0.50_| 0.3
-------------------------
dot_ | 0.48_|_0.45 |_-_

On the vertical axis are products_that_user has, on the horizontal axis those_which the user doesn't_have._The time indicates when_customer bought the product (so_PR1 has been bought this month,_PR2 3_months before_- we assume that products closer to present are more important)._And of course, the measure of_similarity.
Furthermore, dot product between_PR4, PR5_and_time is calculated_to_estimate the_best product (this is simplified, there are_more steps).
So_in this case, I'd offer to_this customer product PR4.
Now_the_struggle I have - How to_evaluate the performance of the recommendation_engine. Often, (R)MSE is used,_but_there_is no ground truth. And_if I remove some products from_customer and then_observe whether the engine recommends them or_not,_the prediction will be skewed