RethinkingCollabora0veFiltering:APrac0calPerspec0veonState-Of-The-ArtResearchBasedon“Real-World”

InsightsandChallenges

NoamKoenigstein

1

RECOMMENDATIONSINMICROSOFTSTORE

2

3

WindowsStore

XboxMarketplace

4

TheXboxMarketplace

5

GrooveRadio

MicrosoE’sWeb-Store

6

ADECADEAGO…

7

ADecadeAgo…TheNeJlixPrize

Thegoal:10%improvementinRMSEoverNeSlix’sCinematchIttooktensofthousandsofpar0cipantsover2years….

8

𝑅𝑀𝑆𝐸=√1/𝑛 ∑𝑖=1↑𝑛▒(𝑦↓𝑖 − 𝑦 ↓𝑖 )↑2   

TheProblemwithRaMngs

•  Theydonotexist!

•  MissingitemsnotatrandomCollaboraMveFilteringandtheMissingatRandomAssumpMon

B.M.Marlin,R.S.Zemel,S.Roweis,M.Slaney

•  Ra0ngsarefuzzyandinfluencedbytheorderofitemsRaMngvs.Preference:AcomparaMvestudyofself-reporMngG.N.Yannakakis,J.Hallam

•  Learningra0ngsisverydifferentfrompersonaliza0on!Yahoo!MusicRecommendaMons:ModelingMusicRaMngswithTemporalDynamicsandTaxonomyGideonDror,NoamKoenigsteinandYehudaKoren

9

IFNOTRMSETHENWHAT?

10

ImplicitFeedbackandRanking•  Collabora0veFilteringforImplicitFeedbackDatasets

Y.Hu,Y.Koren,C.Volinsky

•  Implicit-to-ExplicitOrdinalLogis0cRegressionD.Parra,A.Karatzoglou,X.Amatriain,I.Yavuz

•  BPR-BayesianPersonalizedRankingS.Rendle,C.Freudenthaler,Z.Gantner,andL.S.Thieme

•  RankALS–Alterna0ngLeastSquaresforPersonalizedRankingG.Takacs,D.Tikk

•  CLiMF–ReciprocalRankOp0miza0onYShi,A.Karatzoglou,L.Baltrunas,M.Larson,N.Oliver,A.Hanjalic

11

ONE-CLASSCOLLABORATIVEFILTERINGWITHRANDOMGRAPHS

12

UlrichPaquetandNoamKoenigsteinInterna'onalWorldWideWebConference(WWW'13),May2013,RiodeJaneiro,Brazil.

ProblemFormulaMon

...

N≈10–100K

nod

es

M≈10–100M

nod

es

??

??

BiparMtegraph→Wecareabout?=p(link)

TheHiddenGraph

𝐺={ 𝑔↓𝑚𝑛 },𝐻={ ℎ↓𝑚𝑛 }edges𝑔,ℎ ∈{0,1}

... 𝑔↓𝑚𝑛 =0ℎ↓𝑚𝑛 =1

𝑔↓𝑚𝑛 =0ℎ↓𝑚𝑛 =0

𝐮↓𝑚  𝐯↓𝑛 

𝑝𝑔=1 𝐮,𝐯,ℎ=1 = 𝜎( 𝐮↑𝑇 𝐯) 1

0

𝐮↑𝑇 𝐯

𝑔↓𝑚𝑛 =1ℎ↓𝑚𝑛 =1

BESIDESFEEDBACK:COLDSTART,META-DATA,HYBRID,CONTEXTUAL…

15

XBOXMOVIESRECOMMENDATIONS:VARIATIONALBAYESMATRIXFACTORIZATIONWITHEMBEDDEDFEATURESELECTION

16

NoamKoenigsteinandUlrichPaquetACMConferenceonRecommenderSystems(RecSys'13),October2013,HongKong,China.

MovieFeatures(tags)

17

Categories:

§  Plot§  Mood§  Audience§  TimePeriod

HarryPocerandthePhilosopher'sStone

•  Imaginary•  WizardsandMagicians•  BestFriends

•  Exci0ng•  Humorous•  Danger

•  Kids•  Teens

•  Contemporary•  21stCentury

18

𝑝( 𝑓↓1 , 𝑓↓2 |𝛼=0.01,𝛽=0.01)

19

-10 -5 0 5 10 15-30

-20

-10

0

10

20

30

40

50 Kids

Semi Fantastic

New WaveIndia

PetsAdventure

Foreign

Rescue

Drugs/Alcohol

Semi SeriousAnimal life

Profanity

Serial KillerScary

SwedenSexy Experimental

B&W

Erotic

SuspensefulFamily Gatherings

Cannes Festival Winner

Australia

Grossout Humor

Horror

GROOVERADIO:ABAYESIANHIERARCHICALMODELFORPERSONALIZEDPLAYLISTGENERATION

20

ShayBen-Elazar,GalLavee,NoamKoenigstein,OrenBarkan,HilikBerezin,UlrichPaquet,TalZaccaiACMConferenceonWebSearchandDataMining(WSDM'17),CambridgeUK,February2017.

THEGAPBETWEENCOLLABORATIVEFILTERINGRESEARCHANDREALWORLDRECOMMENDATIONS

21

TheGapBetweenCollaboraMveFilteringandRealRecommenders

•  Diversityvs.accuracy-tradeoff??

•  Popularityvs.personaliza0on

•  Itemfa0gue/freshness–repea0ngitems

•  Serendipity–whenandhowmuchto“surprise”theuser

•  ListRecommenda0ons/pageop0miza0on

•  Predic0ngthefuturevs.influencingtheuser

•  MetricsandEvalua0on

22

TheSalespersonAnalogy

23

BEYONDCOLLABORATIVEFILTERING:THELISTRECOMMENDATIONPROBLEM

24

OrenSarShalom,NoamKoenigstein,UlrichPaquet,HastagiriP.VanchinathanInterna'onalWorldWideWebConference(WWW'16),April2016,Montreal,Canada.

ListRecommendaMonsinXbox360

25

Conclusions•  Thereiss0llagapbetweenmostCFmodelsandtheactualgoalofrecommendersystems

•  Learningindividualuser-itemtuplesorrankingpreferencesisproblema0cbecause:

–  Can’thandlethediversityvs.accuracy“tradeoff”–  Listrecommenda0ons/Pageop0miza0on

•  Learningtopredictfutureeventsfromhistoricaldataisinsufficientbecause:–  Can’thandlebalancingpopularityandpersonaliza0on–  Freshness/ItemFa0gue–  Serendipity

•  RLaloneisnottheul0matesolu0onbecause:

–  Theabundanceofimplicitdata–  Represen0ngthe“tastespace”

•  Offlineevalua0onmetricsareinsufficient

–  Theymeasureourabilitytopredictthefuturebutnotourabilitytochangeit(influencetheuser)

•  Botomline:Wes0llhavealottoworkintheRecSyscommunity!

26

ThankYou!

27

WearelookingforpostdocsinIsrael!!!

Interested?Findmeduringthecoffeebreak….