Table Of ContentExtracting Features from Ratings:
The Role of Factor Models
1
JoachimSelke and Wolf-Tilo Balke
Abstract. Performing effectivepreference-based dataretrieval re- aretheitemvectors)andausermatrix(whosecolumnsaretheuser
quires detailed and preferentially meaningful structurized informa- vectors).
tionaboutthecurrentuseraswellastheitemsunderconsideration. Thesuccessoffactormodelsisusuallyattributedtotheintuition
Acommonproblemisthatrepresentationsofitemsoftenonlycon- thatthecoordinatespaceusedtorepresentitemsandusersactually
sistofmeretechnicalattributes,whichdonotresemblehumanper- is a latent feature space. That is, its dimensions capture the items’
ception.Thisisparticularlytrueforintegralitemssuchasmoviesor perceptual propertiesaswellastheusers’preferencejudgmentsre-
songs.Itisoftenclaimedthatmeaningfulitemfeaturescouldbeex- gardingtheseproperties.Forexample,whenitemsaremovies,thein-
tractedfromcollaborative ratingdata,whichisbecoming available dividualdimensionsaregenerallythoughttomeasure(moreofless)
1
1 throughsocialnetworkingservices.However,thereisonlyanecdotal “obvious” features such as horror vs. romance, the level of sophis-
0 evidence supporting thisclaim; but if it istrue, the extracted infor- tication,ororientationtowardsadults.Forusers,eachcoordinateis
2 mation could very valuable for preference-based data retrieval. In thoughttodescribetherelativedegreeofimportanceattachedtothe
this paper, we propose a methodology to systematically check this respective dimension. This understanding of factor models can be
n
commonclaim.Weperformedapreliminaryinvestigationonalarge foundthroughouttheliterature,forexample,in[2,15,16,18,23].
a
J collectionofmovieratingsandpresentinitialevidence. Although it is intuively appealing, to our knowledge, the corre-
spondence to features has never been systematically proven, but is
2
1 onlyreportedanecdotically.Forexample,Korenetal.[16]performed
1 INTRODUCTION afactorizationontheNetflixmoviedatasetandmanuallyinterpreted
] thefirsttwocoordinatesforselectedmoviesasfollows:
I Recommendersystems[1,17]areoneofthemostprominentapplica-
A
tionsofpreferencehandlingtechnology[6]andahighlyactivearea Someonefamiliarwiththemoviesshowncanseeclearmean-
s. of research. In particular, fueled by theNetflixcompetition and its inginthelatentfactors.Thefirstfactorhasononesidelowbrow
c onemilliondollarprizemoney[2],researchoncollaborativerecom- comediesandhorrormovies,aimedatamaleoradolescentau-
[ mendation techniques [21] has recently made significant advances, dience, while the other side contains drama or comedy with
1 mostnotablythroughtheintroductionoffactormodels[16,22]. seriousundertonesandstrongfemaleleads.Thesecondfactor-
v In collaborative recommender systems, users repeatedly express izationaxishasindependent,criticallyacclaimed,quirkyfilms
8 theirpreferencesforitems,whichusuallyisdonebygivingexplicit onthetop,andonthebottom,mainstreamformulaicfilms.
7 ratingsonsomepredefinednumerical scale.Thisdatacanbemod-
3 eledusingaratingmatrix,whoserowscorrespondtoitems,columns FurtherevidencehasbeenprovidedbyTakácsetal.[23].Afterper-
2 tousers, and entries toratings. Typically, ratings matrices arevery forming a factorization of the Netflix data set, they manually as-
1. sparse,thatis,onlyasmallfractionofallpossibleratingshaveactu- signedlabelstoindividualdimensionsoftheircoordinatespace,such
0 allybeenobserved.Personalizedrecommendationsaregeneratedby asLegendary,Typicalformen,Romantic,andNOTMontyPython.
1 predictingunobserved ratingsfromtheavailabledataand,foreach Inthispaper,weproposeasystematicmethodforstudyingtheco-
1 user,selectingthoseitemsconsideredtobemostappealing. ordinatespacesderivedfromfactormodelsandapplyittheMovie-
v: Most state-of-the-art collaborative recommendation methods— Lens10Mdataset,alargereal-worldcollectionofmovieratings.The
i includingthewinneroftheNetflixPrize—arebasedonfactormod- maincontributionof our workconsistsinlayingimportant ground-
X els,whichareknowntoyieldmuchmoreaccuratepredictionsthan work,onwhichfurtherresearchinrecommendersystemsandprefer-
r traditionalneighborhood-basedmethods[14,15,22,23,24].Infac- encehandlingcanbebuild.Inparticular,weseetwoconcretedirec-
a tor models, each user and each item is represented by a vector in tionsforfuturework:
somesharedrealcoordinatespace.Thevectorsarechosensuchthat
eachobservedratingiscloselyapproximated bythedotproduct of • First,knowingwhatkindofsemanticinformationisextractedby
thecorrespondingitemanduservectors.Theselectionofcoordinates factor models—and how itisrepresented incoordinate spaces—
usuallyisformalizedasanoptimizationproblem.Predictionsforun- willenableadeeperunderstanding ofthesemethods.Ultimately,
observed ratings are generated by computing the respective scalar these findings may lead to a more systematic development and
products. Equivalently,thisapproach canbeseenasafactorization refinement of recommender systems. In particular, a systematic
oftheratingmatrixintotheproductofanitemmatrix(whoserows assessment of semantic structuresprovides anadditional way of
evaluating the effectiveness of factor-based recommenders. This
1 Institut für Informationssysteme, Technische Universität Braunschweig, wouldperfectlycomplement traditionalevaluationmethods[11],
Germany whichfocusonpredictiveaccuracy.
• Second, we believe that factor models might be apowerful tool More advanced versions of the SVD model exclude systematic
forautomaticallyextractingmeaningfuldescriptionsofotherwise rating deviations from the factorization and model them explicitly
hard-to-describeitemssuchasmoviesorsongs—particularly,es- using new variables. Belland Koren [3] propose toestimaterating
sentialfeaturesofmoviescannotbecharacterizedatallbypurely ri,uby
technicalfeaturessuchasruntime,language,orreleasedate.2But d
givenacoordinaterepresentationofmoviesthatmatcheshuman rˆi,u =µ+δi+δu+ ai,rbr,u,
perception, the fullmachinery developed inpreference handling Xr=1
researchcanbeapplied[6,9].Forexample,clusteringtechniques wheretheconstantµdenotesthemeanofallobservedratings;δiand
can give user an initial high-level impression of the available δuareI+U newmodelparametersexpressingsystematicitemand
items,itemrankings canbe learntfromordinal preference state- userdeviationsfromµ.Again,theparametersarechosenaccording
ments[10]orutilities[5],andthebestitemscanberetrievedby toaregularizedleastsquaresproblem:
meansofTop-kalgorithms[12].
d
min SSE R,Rˆ +λ a2i,r+b2r,u +δi+δu .
basSeidncreetoriuervaplrtiemcahrnyiqrueesseatrochiteimntecroelsltecliteiosnisn,ianptphliysinpgapperrefwereewnciell- A,B,δ⋆ (cid:0) (cid:1) (i,Xu)∈R Xr=1(cid:0) (cid:1) !
concentrate on evaluating the semantic structures contained in the Therationaleunderlyingthisapproach—whichwerefertoasδ-SVD
itemmatrix A. Performing a similar analysis of the user matrixB inthefollowing—isthattheremovalofitem-anduser-specificgen-
mayrequireentirelydifferentmethods. eraltrends fromthefactorization allowstofocuson moresophisti-
Thepaperisstructuredasfollows:Afterintroducingnotationand catedratingpatterns.
reviewing the most important factor models, we develop general Thethirdbasicfactormodelbeingrelevanttoourworkperforms
guidelinesonhowtoevaluatecoordinatespacesforsemanticinfor- anon-negativefactorizationoftheratingmatrix[23].Itisidentical
mation.Then,weillustratehowtoapplytheseguidelinestotheeval- totheregularizedSVDmodeluptotheadditionalconstraintthatall
uationoffactorspacesgeneratedfrommovieratingdataandperform entriesofAandB mustbenon-negative. Extendingthismodelby
experimentsontheMovieLens10Mdataset. explicititemandusersdeviationsisnotreasonablesincethiswould
requirenegativeentriesinAandBtoapproximateRcloseenough.
The non-negative matrix factorization model aims at creating a co-
2 PRELIMINARIES
ordinate space in which effectsof different dimensions on the esti-
In the following, we use the variables i and j to identify items, matedratingscannotcancelouteachother.Henceforth,wereferto
whereasuandvdenoteusers.WearedealingwithratingsgiventoI thismodelasNNMF.
itemsbyU users.LetR=(ri,u)∈{R∪∅}I×U bethecorrespond-
ing rating matrix, where ri,u = ∅, if item i has not been rated by 3 EVALUATINGCOORDINATESPACES
useru;otherwise,ri,u expressesthestrengthofuseru’spreference Givenanitem–featurematrixA ∈ RI×d generatedbysomefactor
for item i. Ratings are usually limited to a fixed integer scale (for
model,howcanwedeterminewhethertheitems’coordinatesinthis
example,onetotenstars).Moreover,R= (i,u)|ri,u 6=∅ isthe
d-dimensionalspaceresemblea“semanticallymeaningful”pattern?
setofallitem–userpairsforwhichratingsareknown.Letnbethe
(cid:8) (cid:9) Themoststraightforwardapproachconsistsinextendingandsystem-
totalnumberofratingsobserved(thecardinalityofR).Typically,n
atizingthecasual investigations described inthe introduction. This
isverysmallcomparedtothenumberofpossibleratingsI ·U (for
example,intheNetflixdatasetitis n ≈1.4%). could easily be done by presenting the item coordinate space to a
I·U numberofdifferentpeopleandaskingthemtolabelitsdimensions.
Givensometargetdimensionalityd,thebasicideaunderlyingfac-
tormodelsistofindmatricesA=(ai,r)∈RI×dandB=(br,u)∈ Thecorrespondencebetweenthegenerateditemcoordinatesandhu-
Rd×U such that their product Rˆ = A·B closely resembles R on manperceptioncould,forexample,bedonebymeasuringthedegree
ofconsensusamongpeopleortheaveragetimeneededtocomeup
all known entries. To quantify this notion of “close resemblance,”
withadequatelabels.
thesumofsquarederrors(SSE)ispopularlychosen.TheSSEdiffer-
encebetweentheratingmatrixRanditsestimationRˆ = (rˆi,u)is Althoughthiskindofinvestigationseemsveryreasonable,itcon-
tainssomesevereflaws,whichcannotbefixedbycarefulstudyde-
definedas
SSE R,Rˆ = ri,u−rˆi,u 2. sign:
(cid:0) (cid:1) (i,Xu)∈R(cid:0) (cid:1) 1. Thedimensionalitychoseninmostapplicationsoffactormodels
Factor models are typically formulated as optimization problems typicallyrangesbetweend=10andd=100.Acomprehensive
over A and B, inwhich the SSE(or some other measure) isto be analysisoftheresultingdatasetswouldrequiretheuserstocom-
minimized. prehendhigh-dimensionalspaces,whichisimpossibleevenwhen
ProbablythemostpopularfactormodelisBrandynWebb’sregu- usingadvancedvisualizationtechniques.
larized SVD model [16, 18], in which A and B aredefined as the 2. Duetohindsightbias,givenenoughtime,userswillbeabletoas-
solutionoftheleastsquaresproblem signafittinglabeltoalmostanydimensionofthecoordinatespace.
Chancesaregoodthatthiseffectaccountsforratherquestionable
d labelssuchasNOTMontyPython.
min SSE R,A·B +λ a2i,r+b2r,u . 3. By using free association to name dimensions, the collection of
A,B
(cid:0) (cid:1) (i,Xu)∈RXr=1(cid:0) (cid:1) resultinglabelstendtoshowahighvariabilityandreflectindivid-
ualdifferencesbetweenusers.Toproducestatisticallysignificant
Here,λ≥0isaregularizationconstantusedtoavoidoverfitting.
results,either thesample sizemust be extended (whichrequires
2 Acomplementaryapproachtoclosingthissemanticgapiscontent-based more study participants and results in higher costs), or the vari-
imageandvideoretrieval[8]. abilitymust be reduced, for example, by trainingparticipants to
useanestablisheddomain-specificvocabularytoarticulatethese- elementsare ordered by increasing magnitude. Moreover, thediag-
manticpropertiestheyrecognizeinthedata(whichalsoincreases onal matrix S can be eliminated from this factorization by setting
timeandeffort). X = U′V′,whereU′ = US12 andV′ = US12.ThematricesU′
4. Typically,therearemanynear-optimalsolutionstotheabovemen- andV′ areuniqueifalldiagonalelementsofS havebeenmutually
tionedoptimizationproblems,whichcanbetransformedintoone different.
another by rotation of the coordinate axes. This is because, for Inoursetting,wewillapplythesingularvaluedecompositionto
any invertible matrix M ∈ Rd, the solution pairs (A,B) and transformtheproductX =A·BintoanewproductA′·B′asjust
(AM,M−1B) produce the same SSE. Although regularization described.Sinceratingdatatendstobevery“noisy,”wecansafely
usuallyenforcesthetheoreticalexistenceofauniqueoptimalsolu- assumethat(A′,B′)isauniquerepresentationof(A,B);wedidnot
tionpair,inpracticetheenormousproblemsizeoftenallowsonly encounteranycounterexamplesduringourexperimentsonlargereal-
findingoneofthemanynear-optimalsolutions.Consequently,the worldratingdata.Moreover,anyequivalentpair(AM,M−1B)also
direction of the coordinate axes is completely arbitrary, which getstransformedinto(A′,B′),whichwedefineasthecorrespond-
makesthetaskofassigninglabelsahopelessundertaking. ingstandardrepresentation.Itcanbecomputedefficientlyusingthe
productdecompositionalgorithmproposedin[7,Sec.3].
3.1 SomeGuidelines
3.2 UseCase:MovieRatings
Inthissection,wedeviseasetofguidelinesonwhichtobasemore
Based on these guidelines, we now present a concrete method for
appropriateapproachestotheanalysisofcoordinatespaces.
performing a basic evaluation of coordinate spaces generated from
movieratings.Ourfocusrestsonimmediateapplicability,sowere-
• Intheviewofproblems(1)and(4),werecommendtoavoidany
latetheitemcoordinatestoreferencedatathatisalreadyavailable.
direct human interaction with item coordinates. Instead, human
Thereferencesourceforallkindsofmovie-relatedinformationis
inputshouldconcentrateondescribingitemproperties,whichin
IMDb,theInternetMovieDatabase4,whichcurrentlycoversabout
turnarerelatedtocoordinatesaswellascomparedbyalgorithmic
1.6milliontitles.MostofIMDb’sdatahasbeencreatedwiththehelp
means.
ofitsusers.Therefore,alargeproportionoftheavailablecontentcan
• Theonlyeffectivewaytoeliminatehindsightbias(2)iscollecting
freelybedownloadedandusedfornon-commercialpurposes5.Based
feedbackonitemsbeforegeneratingandpresentinganyinforma-
onthiscomprehensivedata,oneshouldbeabletocross-referenceany
tionextractedbythefactormodelsunderconsideration.
collectionofmovieratingswithIMDb.
• To resolve problem (3), we primarily recommend to adapt a
Forthesemanticevaluationswearegoingtoperform,thefollow-
domain-specificvocabularytoallowastructurizeddescriptionof
ingattributesoftitlesmayprovehelpful:genres,certifications(e.g.,
items. For example, to characterize music, the rich vocubulary
USA:PGforparentalguidancesuggested),yearofrelease,andplot
developed byallmusic3 seemsappropriate;amongst others,itin-
keywords. To illustratethe general procedure, wewill only exploit
cludesverydetailedinformationaboutgenres,styles,moods,and
genreinformationinthispaper.Extendigourmethodtoothertypes
connectionsbetweenartists.Sincethiskindofsemanticinforma-
ofsemanticinformationisstraightforward.Checkingthecorrespon-
tioncanbe(oralreadyhavebeen)providedbyasmallnumberof
dence between genres and item coordinates also makes up a good
experts and usually is littleprone to debate, it is easy to assem-
firsttestofwhetheratleastsomebasicsemanticpropertiesofmovies
ble and work with. In later stages of analysis, unrestricted user
arerepresentedincoordinatespaces,whichisexactlythepurposeof
feedback may be included to reveal the position and extent of
thecurrentwork.
morefine-grainedandrathersubjectiveconceptsinthecoordinate
IMDb recognizes 28 different genres, from Action to Western,
space.
where each movie may belong to multiple genres. The assignment
ofgenresisdonebyIMDb’sexpertstaffincooperationwithIMDb
Wealsoproposetoapplyastandardizationproceduretothegener-
users.Toenforceconsistency,thisprocessisbaseduponacollection
atedcoordinatespace.Thisisforthefollowingreasons:First,recall
ofpubliclyavailableguidelines6.Therefore,thisdatasourcematches
that,foranyinvertiblematrixM ∈Rd,thesolutionpairs(A,B)and
(AM,M−1B)areequivalent;toenablecomparisonsbetweendiffer- therequirementsdevelopedintheprevioussection.
Toanalyzewhetherthedistributionofgenresincoordinatespace
ent factor models andeven different runsof thesameoptimization
displaysanysignificantpattern,weturntoestablishedclassification
algorithms,weneedtodefineonesolutionpairasthestandardrepre-
algorithms, which explicitly have been designed to exploit any rel-
sentation.Second,toenableabetterseparationofdifferenteffectsin
evantpatternsinthedataifthereareany.Inparticular,wepropose
thedata,theaxesoftheitem(anduser)coordinatespaceshouldbe
tomeasurethedegreeofadherencetoapatternbytheclassification
chosentobeorthogonal.Moreover,axesshouldbeorderedaccording
accuracy shown by these algorithms when predicting the genre of
totheirrelativeimportance(measuredbythevarianceofdataalong
moviesbasedontheircoordinates.Inessence,wetransformouranal-
eachaxis);thatis,thefirstdimensionshouldbeassignedtothemost
ysisintoasequenceofbinaryclassificationproblems(oneforeach
importantaxis.
genre), which enables us to build on solid grounds. Following the
The perfect tool for matching these requirements is the singular
common methodology, weusecross-validation; that is,accuracy is
value decomposition, a well-known matrix factorization technique
measuredonadataset,whichisindependentoftheoneusedtotrain
fromlinearalgebra,whichinspiredtheSVDfactormodel.Itisbased
theclassifier.Byapplying proven techniques tocounter overfitting,
onthefactthat,foranyrank-dmatrixX ∈I×U,thereisacolumn-
orthonormal matrixU ∈ RI×d,adiagonal matrixS ∈ Rd×d,and ourapproachalsoovercomesanypossibleproblemsrelatedtohind-
sightbias.
arow-orthonormal matrixV ∈ ×UsuchthatX = USV.Byre-
orderingrowsandcolumns,S canbechosensuchthatitsdiagonal 4http://www.imdb.com
5http://www.imdb.com/interfaces#plain
3http://www.allmusic.com 6http://www.imdb.com/updates/guide/genres
For a start, we selected two popular classification algorithms, 4 EXPERIMENTSONMOVIELENS10M
whichareabletodetectdifferentkindsofpatternsinthedata:support
vectormachinesandkNN-classifiers. We applied our approach to the MovieLens10M data set 7, which
Supportvectormachineswillbeusedintwodifferentflavors:first, consists of about 10 million ratings collected by the online movie
using a linear kernel (refered to as SVM-lin), and second, using a recommenderserviceMovieLens8.Afterpostprocessingtheoriginal
Gaussian radial basis function kernel (SVM-RBF). Linear support data (removing one non-existing movie, merging several duplicate
vector machines will show a high classification accuracy if most movie entries, and removing movies that received less than 20 rat-
moviesoftherespectivegenrearegroupedatonesideofthedataset, ings),ournewdatasetconsistsof9,984,419ratingsof8938movies
whichcanbeseparatedfromallremainingmoviesbyahyperplane. providedby69878users.Theratingsusea10-pointscalefrom0.5
Forexample,thiscanbeusedtodisprovethehypothesisthatthereex- (worst)to5(best).Eachusercontributedatleast14ratings.
istsadirectioninthecoordinatespacealongwhich,say,theamount OuranalysisrequiresthegenreinformationmaintainedbyIMDb,
ofaction,increasesmonotonically.Incontrast,theSVM-RBFclassi- so we had to map each movie in the data set to its corresponding
fierdetectswhethergroupsofmovieswiththesamegenretendtobe IMDb entry. This task has been simplified a lot by the fact that
locatedinclosevincinity. all items inthe MovieLens10M data set are relatively well-known
kNN-classifiersperformwellifthedistancebetweenmovieshav- moviesdeveloped for cinema.9 Wemapped about 8000 movies au-
ingthesamegenretypicallyissmallerthanthedistancetomovies tomatically by comparing titles and release years; the remaining
nothavingthisgenre.Therefore,theycanbeusedtocheckwhether movieshavebeenassignedmanuallyorsemi-automatically.
genres form spatially separated patterns in coordinate space. Since Toavoidtheproblemoflearningfromverysmallsamplesfornow,
factormodelsarenotbasedonanotionofproximity,itisnotclear wedidnotuseall28genresdistinguishedbyIMDb.Instead,wetake
whatmeasureofdistancesuitsfactormodelsbest.Wewilltryoutthe only those genres into consideration that have been assigned to at
followingfourmeasures:Euclideandistance,standardizedEuclidean least5%ofallmoviesinourdataset.Table1listsallremaining13
distance(where,toensureequallyweighteddimensions,coordinate genres and their relative frequencies. On average, 2.3 genres have
valuesaredividedbythestandarddeviationofthedatawithrespect beenassignedtoeachmovie.
each dimension), negative scalar product (which essentially adapts
themethodofratingpredictiontomeasuredistance),andcosinesim- Genre % Genre %
ilarity(whichismonotonicallyrelatedtotheanglebetweentwovec-
Action 16.0 Horror 10.1
tors).
Adventure 12.7 Mystery 9.1
Toevaluate thetruebenefit of coordinate spaces generated from Comedy 38.2 Romance 25.2
factormodels,wepropose thefollowingbaseline,whichisderived Crime 16.6 Sci-Fi 8.6
fromtraditionalneighborhood-basedrecommendationmethods[20] Drama 54.6 Thriller 24.2
Family 8.4 War 5.2
andconstructedasfollows:First,foranyitemsiandj,wecompute
Fantasy 8.3
theirPearsoncorrelationcoefficient
Table1. Relativefrequenciesofgenres.
̺i,j = u∈Ri,j(ri,u−µi,j)(rj,u−µj,i) ,
u∈RPi,j(ri,u−µi,j)2 u∈Ri,j(rj,u−µj,i)2
qP qP 4.1 GeneratingCoordinateSpaces
where Ri,j isthe set of all users who rated both i and j, and µi,j Weimplemented eachofthefour coordinateextractionmethods in
is the mean rating given to item i by users who rated both i and
MATLABandexecutedthemonourratingdata.
j. IfRi,j isempty, then̺i,j isundefined. ThePearsoncorrelation ForSVD,δ-SVD,andNNMF,wefollowedtheliteratureandused
coefficient̺i,j measuresthetendencyofuserstorateitemsiandj anoptimizationprocedurebasedongradientdescent;toreducecom-
similarly.Toavoidbiasedestimatesincaseswhereni,j = |Ri,j|is putation time, we applied the Hessian speedup proposed in [19].
verysmall,wederiveanewmeasureofsimilarity
Adaptingthecommonmethodology,wechosetheregularizationpa-
rameterλbycross-validationsuchthattheSSEisminimizedonran-
si,j = ni,j ·̺i,j domlychosentestsets.Weendedupwithavalueofλ = 0.04for
ni,j+λ eachofthethreealgorithms.
Since optimization by gradient descent is known to get stuck in
from̺i,jbyshrinkingtowardszero[15].Here,λ≥0isaregulariza- localextremaofthefunctiontobeminimized,weranthethreepro-
tionparameter.Finally,wecarryoverthesesimilarityintodistances cedures at least three times, each with different initial coordinates,
byapplyingalogarithmictransformation: whichhavebeenchosenrandomly.Foreachresult,wecomputedthe
standardized solution pair asdescribed intheprevious section. We
di,j =−ln 1+si,j . fsoigunnidfictahnattlythaeftseorlsuttainodnasrdgieznaetiroante.dThbiyseinadcihcaetxetsrathcatotroudrocnoootrddiinfafeter
2
(cid:18) (cid:19) spacesmatchtheuniquesolutionofeachoptimizationproblem.
ForourMDSprocedure,weusedtheregularizationconstantλ=
Toderivead-dimensional coordinatespaceinwhichitemsiand j
20, which we determined by adapting the recommendation Koren
approximately have distance di,j, we use metric multidimensional
scaling[4].Sinceneighborhood-basedrecommendationmethodsare
7http://www.grouplens.org/node/73
usuallyoutperformedbyfactormodels,weexpectourbaselinecoor- 8http://www.movielens.org
dinatespacetobefarinferiortothoseconstructedusingfactormod- 9ThisisthereasonwhywedidnotconsidertheNetflixdataset.Itconsistsof
els.WerefertoourbaselinemodelasMDS. allkindsofDVDtitles,whichoftenlackaclearcorrespondenceinIMDb.
gavefortheNetflixdataset[15].Thecoordinateshavebeengener-
SVD-10 SVD-50 SVD-100
atedbyMATLAB’smdscalefunctionusing themetricstresscri-
SVM-lin 0.08 0.18 0.20
terion. Since in our data set about 14 percent of all movie–movie
SVM-RBF 0.15 0.23 0.25
pairshad noratersincommon, wetreatedtherespective entriesof 1NN-Eucl −0.24 −0.21 −0.19
thedistancematrixasmissingdata. 3NN-Eucl 0.01 0.05 0.04
Tomeasuretheeffectofdimensionality,wegeneratedthreediffer- 9NN-Eucl 0.12 0.16 0.14
entcoordinatespaceswitheachextractorbyvaryingtheparameterd. 1NN-sEucl −0.25 −0.27 −0.31
3NN-sEucl 0.01 0.00 −0.06
Wechosed=10,d=50,andd=100.
9NN-sEucl 0.12 0.12 0.04
1NN-scal −0.42 −0.30 −0.30
3NN-scal −0.16 −0.03 −0.03
4.2 ApplyingtheClassifiers 9NN-scal 0.01 0.11 0.12
1NN-cos −0.25 −0.18 −0.16
In total, we used 14 different classifiers to evaluate each of the 12 3NN-cos 0.00 0.06 0.06
9NN-cos 0.12 0.17 0.16
coordinatespaceswithrespecttoeachofthe13genres.
We implemented the two support vector machine classifiers by Table2. KappasforcoordinatesgeneratedbySVD.
soft-margin SVMs with parameters C = 4 and (for SVM-RBF)
γ = 0.1, which have been determined by cross-validation to max- δ-SVD-10 δ-SVD-50 δ-SVD-100
imizeclassificationaccuracy.
SVM-lin 0.07 0.16 0.18
Each of the four different kNN-classifiers will be applied to the SVM-RBF 0.13 0.20 0.23
data sets with three different choices of k. To measure whether 1NN-Eucl −0.26 −0.26 −0.26
moviesofthesamegenretendtooccur inlargergroups, wechose 3NN-Eucl −0.01 0.01 −0.02
9NN-Eucl 0.11 0.12 0.08
k =1,k =3,andk =9.Inthefollowing,wewillrefertothese12
1NN-sEucl −0.26 −0.29 −0.36
classifiersaskNN-Eucl,kNN-sEucl,kNN-scal,andkNN-cos. 3NN-sEucl 0.00 −0.03 −0.11
Toenablecomparisonsamongclassifiersanddatasets,wegener- 9NN-sEucl 0.11 0.09 −0.01
ated20pairsoftrainingandtestsets,eachbyrandomlychosing40% 1NN-scal −0.41 −0.28 −0.22
of all movies for training and 10% (of the remaining movies) for 3NN-scal −0.06 0.02 0.06
9NN-scal 0.05 0.13 0.16
testing. For each of the resulting 2184 combinations of coordinate 1NN-cos −0.26 −0.19 −0.16
spaces,classifiersandgenres,weusethesame20pairsofitemsets 3NN-cos 0.00 0.07 0.09
fortrainingandtesting.Ineachcase,wemeasuredtheclassification 9NN-cos 0.12 0.18 0.19
accuracy.Allresultsreportedbelowareaveragesoverthe20runs.
Table3. Kappasforcoordinatesgeneratedbyδ-SVD.
4.3 Results NNMF-10 NNMF-50 NNMF-100
SVM-lin 0.02 0.05 0.11
Probably the most popular way of assessing a classifier’s perfor- SVM-RBF 0.02 0.09 0.14
mance is measuring its accuracy, that is, the fraction of test items 1NN-Eucl −0.56 −0.47 −0.41
3NN-Eucl −0.20 −0.16 −0.13
which have been classified correctly. However, in our setting, this
9NN-Eucl −0.02 0.01 0.02
measure isnot very helpful. Toseethis, recall that the relativefre- 1NN-sEucl −0.56 −0.47 −0.45
quencyofgenresisverydifferentinourdataset.Forexample,over 3NN-sEucl −0.20 −0.16 −0.16
halfofallmoviesbelongtothegenreDrama,butthereareonlyabout 9NN-sEucl −0.02 0.01 0.00
5%War movies.Whileattaininganaccuracyof95%wouldbesig- 1NN-scal −0.37 −0.34 −0.34
3NN-scal −0.11 −0.10 −0.09
nificantforthegenreDrama,itcaneasilybeachievedforthegenre 9NN-scal −0.02 0.00 0.02
War just by classifying any movie as non-War. To enable compar- 1NN-cos −0.56 −0.45 −0.41
isonsacrossgenres,weproposetouseamodifiedversionofCohen’s 3NN-cos −0.20 −0.15 −0.13
kappameasure. 9NN-cos −0.03 0.02 0.03
Anyresultofabinaryclassificationtaskcanbedescribedbyfour
Table4. KappasforcoordinatesgeneratedbyNNMF.
numbers, which sum up to 1: the fraction of true positives (α ),
tp
the fraction of false positives (α ), the fraction of false negatives
fp MDS-10 MDS-50 MDS-100
(α ), and the fraction of true negatives (α ). Accuracy is defined
fn tn SVM-lin −0.16 0.15 0.19
as acc = α + α . Moreover, the accuracy of a static majority-
tp tn SVM-RBF 0.03 0.16 0.17
basedclassifier(whichalwaysreturnsthelabelofthemorefrequent 1NN-Eucl −0.29 −0.19 −0.18
class) is accmaj = max{αtp +αfn,αfp +αtn}. We propose to use 3NN-Eucl −0.01 0.06 0.06
thiskindofnaiveclassifierfornormalizingtheaccuracyanddefine 9NN-Eucl 0.13 0.18 0.18
1NN-sEucl −0.29 −0.23 −0.29
κ = (acc−acc )/(1−acc ). This measure expresses a clas-
maj maj 3NN-sEucl −0.01 0.05 −0.01
sifier’srelativeperformancewithrespecttothemajority-basedclas-
9NN-sEucl 0.13 0.17 0.12
sifier. If acc = 1 then κ = 1, if acc > accmaj, then κ > 0, if 1NN-scal −0.29 −0.19 −0.18
acc=acc ,thenκ=0,andifacc<acc ,thenκ<0. 3NN-scal −0.01 0.07 0.08
maj maj
Bymeasuringaccuracy intermsof κ,wecan averageclassifica- 9NN-scal 0.12 0.18 0.18
1NN-cos −0.28 −0.18 −0.16
tionperformance over different genres. Tables2–5 report themean
3NN-cos 0.00 0.07 0.08
κsoverall260classificationresultsobtainedforeachcombination 9NN-cos 0.13 0.19 0.19
of coordinate space and classifiertype. Allentries larger than 0.10
havebeenmarkedinboldface.Wecanobservethefollowing: Table5. KappasforcoordinatesgeneratedbyMDS.
• The coordinate space derived by NNMF does not contain much WeappliedourapproachtotheMovieLens10Mdatasetandfound
helpfulinformationaboutgenresthatcanbeexploitedbyourclas- initialevidenceforthisclaim.
sifiers.Theperformanceinallotherspacesissignificantlybetter. Ourresultsencourageustofollowthislineofresearchinseveral
• Except for NN-sEucl, classification performance generally im- ways. First, we would like to investigate whether our results also
proveswithincreasingdimensionality.However,thedifferencein carryovertomoreadvancedandcomplexfactormodels,whichhave
performance between d = 10 and d = 50 is much larger than beenproposedveryrecently[13,15].Itwouldalsointerestingtosee
theonebetweend = 50andd = 100.Thisindicatesthatouror- whatmoretraditionalmethodssuchasmultidimensionalscalingcan
deringofdimensionsduringstandardizationindeedcapturessome contributetotheproblemoffeatureextractionfromratingdata,since
notionofrelativeimportance.Thisisprobablyalsothereasonfor our resultsindicatethat thesemethods cansucessfullybemodified
NN-sEucl’sdecreasing performance withgrowingd;treatingall foruseinournewsetting.
dimensionsequallyseemstooverweightinformationfromdimen-
sionsattheendofthelist.
• The SVM-RBF classifier slightly outperforms SVM-lin, but is References
comparable in performance to 9NN-Eucl, 9NN-scal, and 9NN-
cos.Thisindicatesthatgenresindeedtendtoclusterincoordinate [1] GediminasAdomaviciusandAlexanderTuzhilin,‘Towardthenextgen-
erationofrecommendersystems:Asurveyofthestate-of-the-art and
spaces,evenwithrespecttodifferentmeasuresofdistance.
possibleextensions’,IEEETransactionsonKnowledgeandDataEngi-
• TheNN-classifiersdisplaybadperformancefork=1andk=3, neering,17(6),734–749,(2005).
whichindicatesthat,althoughmoviesofthesamegenreroughly [2] Robert M. Bell, Jim Bennett, Yehuda Koren, and Chris Volinsky,
occurinclusters,eachclusterusuallyalsocontainsmoviesthatdo ‘Themilliondollarprogrammingprize’,IEEESpectrum,46(5),28–33,
(2009).
nothaveassignedtherespectivegenre.
[3] RobertM.BellandYehudaKoren,‘Scalablecollaborativefilteringwith
• In contrast to our expectations, the performance in coordinate
jointlyderivedneighborhoodinterpolationweights’,inProceedingsof
spaces generated by factor models is comparable to the perfor- ICDM2007,pp.43–52.IEEEComputerSociety,(2007).
manceshownonourbaselinecoordinatespaceMDS. [4] IngwerBorgandPatrickJ.F.Groenen,ModernMultidimensionalScal-
ing:TheoryandApplications,Springer,secondedn.,2005.
[5] CraigBoutilier,KevinRegan,andPaoloViappiani,‘Preferenceelicita-
Moreover, the results suggest that the performance of kNN-
tionwithsubjectivefeatures’,inProceedingsofRecSys2009,pp.341–
classifiersmightevenfurtherincreaseforlargervaluesofk.Tocheck 344.ACM,(2009).
this,weperformedsomepreliminarytestswithk≈20,buthavenot [6] RonenI.BrafmanandCarmelDomshlak,‘Preferencehandling:Anin-
beenabletoconfirmthisconjective. troductorytutorial’,AIMagazine,30(1),58–86,(2009).
[7] ZlatkoDrmacˇ,‘Accuratecomputationoftheproduct-inducedsingular
Wealsoinvestigatedtheinfluenceofindividual genresonclassi-
value decomposition with applications’, SIAMJournal onNumerical
ficationperformance; asan example, theresultsfor SVM-RBFare
Analysis,35(5),1969–1994,(1998).
reportedinTable6.Entrieslargerthan0.20havebeenindicated.We [8] PeterEnserandChristineSandom,‘Towardsacomprehensivesurvey
canseethatsomegenres,suchasHorrorandDrama,canclearlybe ofthesemanticgapinvisualimageretrieval’,inProceedingsofCIVR
identified by the classifier, while others cannot. We have expected 2003,volume2728ofLNCS,pp.291–299.Springer,(2003).
[9] JohannesFürnkranzandEykeHüllermeier,‘Preferencelearning’,Kün-
muchbetterperformanceonclear-cutgenressuchasWar.
stlicheIntelligenz,2005(1),60–61,(2005).
[10] Ralf Herbrich, Thore Graepel, and Klaus Obermayer, ‘Large margin
SVD-100 δ-SVD-100 NNMF-100 MDS-100 rankboundariesforordinalregression’,inAdvancesinLargeMargin
Classifiers,115–132,MITPress,(2000).
Action 0.34 0.31 0.22 0.22 [11] Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and
Adventure 0.13 0.12 0.08 0.00 John T. Riedl, ‘Evaluating collaborative filtering recommender sys-
Comedy 0.45 0.42 0.25 0.42 tems’,ACMTransactionsonInformationSystems,22(1),5–53,(2004).
Crime 0.08 0.06 −0.01 0.00 [12] IhabF.Ilyas,GeorgeBeskales,andMohamedA.Soliman,‘Asurvey
Drama 0.47 0.43 0.37 0.44 oftop-kquery processing techniques inrelational database systems’,
Family 0.43 0.46 0.31 0.34 ACMComputingSurveys,40(4),(2008).
Fantasy 0.03 0.05 0.01 0.00 [13] YehudaKoren,‘Factorizationmeetstheneighborhood:Amultifaceted
Horror 0.56 0.54 0.31 0.61 collaborative filteringmodel’,inProceedingsofKDD2008,pp.426–
Mystery 0.06 0.04 −0.00 0.00 434.ACMPress,(2008).
Romance 0.11 0.10 −0.00 0.00 [14] YehudaKoren,‘Collaborativefilteringwithtemporaldynamics’,Com-
Sci-Fi 0.23 0.20 0.09 0.00 municationsoftheACM,53(4),89–97,(2010).
Thriller 0.31 0.27 0.14 0.15 [15] YehudaKoren,‘Factorintheneighbors:Scalableandaccuratecollabo-
War 0.05 0.06 −0.00 0.00 rativefiltering’,ACMTransactionsonKnowledgeDiscoveryfromData,
4(1),(2010).
Table6. KappasforSVM-RBFbygenre.
[16] YehudaKoren,RobertBell,andChrisVolinsky,‘Matrixfactorization
techniquesforrecommendersystems’,IEEEComputer,42(8),30–37,
In summary, these preliminary experiments suggest that the co- (2009).
[17] DonMonroe,‘Justforyou’,Communications oftheACM,52(8),15–
ordinatespaces derivedbySVD,δ-SVD,andMDS indeedcontain
17,(2009).
somesignificantsemanticinformationabouttherepresentedmovies. [18] Gregory Piatetsky-Shapiro, ‘Interview with Simon Funk’, ACM
However,thesituationisbyfarnotasclearasclaimedbythelitera- SIGKDDExplorationsNewsletter,9(1),38–40,(2007).
ture. [19] Tapani Raiko, Alexander Ilin, andJuhaKarhunen, ‘Principal compo-
nent analysis for large scale problems with lots of missing values’,
in Proceedings of ECML 2007, volume 4701 of LNAI, pp. 691–698.
5 CONCLUSIONANDOUTLOOK Springer,(2007).
[20] BadrulSarwar,GeorgeKarypis,JosephKonstan,andJohnReidl,‘Item-
basedcollaborative filteringrecommendationalgorithms’,inProceed-
In the current paper, we presented a general methodology for sys-
ingsofWWW2001,pp.285–295.ACMPress,(2001).
tematicallyanalyzingwhethercoordinatespacesgeneratedfromfac- [21] J.BenSchafer,DanFrankowski,JonHerlocker,andShiladSen,‘Col-
tormodelscontainsemanticinformation,asitiscommonlyclaimed. laborativefilteringrecommendersystems’,inTheAdaptiveWeb:Meth-
odsandStrategiesofWebPersonalization,volume4321ofLNCS,291–
324,Springer,(2007).
[22] AjitP.SinghandGeoffreyJ.Gordon,‘Aunifiedviewofmatrixfactor-
izationmodels’,inProceedingsofECMLPKDD2008:PartII,volume
5212ofLNCS,pp.358–373.Springer,(2008).
[23] Gábor Takács,István Pilászy, Bottyán Németh, and Domonkos Tikk,
‘Scalablecollaborativefilteringapproachesforlargerecommendersys-
tems’,JournalofMachineLearningResearch,10,623–656,(2009).
[24] MarkusWeimer,AlexandrosKaratzoglou,andAlexSmola,‘Improving
maximummarginmatrixfactorization’,MachineLearning,72(3),263–
276,(2008).
