Tilburg University Recommender systems for social bookmarking Bogers, AM

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Tilburg University
Recommender systems for social bookmarking
Bogers, A.M.
Publication date:
2009
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Bogers, A. M. (2009). Recommender systems for social bookmarking. TICC Dissertation Series 10.
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Recommender Systems for
Social Bookmarking
PROEFSCHRIFT
ter verkrijging van de graad van doctor
aan de Universiteit van Tilburg,
op gezag van de rector magnificus,
prof. dr. Ph. Eijlander,
in het openbaar te verdedigen ten overstaan van een
door het college voor promoties aangewezen commissie
in de aula van de Universiteit
op dinsdag 8 december 2009 om 14.15 uur
door
Antonius Marinus Bogers,
geboren op 21 september 1979 te Roosendaal en Nispen
Promotor:
Prof.dr.A.P.J.vandenBosch
Beoordelingscommissie:
Prof.dr.H.J.vandenHerik
Prof.dr.M.deRijke
Prof.dr.L.Boves
Dr.B.Larsen
Dr.J.J.Paijmans
The research reported in this thesis has been funded by SenterNovem / the Dutch Ministry
ofEconomicAffairsaspartoftheIOP-MMIÀProposproject.
SIKSDissertationSeriesNo. 2009-42
The research reported in this thesis has been carried out under the auspices of SIKS, the
DutchResearchSchoolforInformationandKnowledgeSystems.
TiCCDissertationSeriesNo. 10
ISBN978-90-8559-582-3
Copyright(cid:13)c 2009,A.M.Bogers
All rights reserved. No part of this publication may be reproduced, stored in a retrieval sys-
tem, or transmitted, in any form or by any means, electronically, mechanically, photocopying,
recordingorotherwise,withoutpriorpermissionoftheauthor.
“
TheWeb,theysay,isleavingtheeraofsearchandenteringoneofdiscovery.
What’sthedifference? Searchiswhatyoudowhenyou’relookingforsome-
thing. Discoveryiswhensomethingwonderfulthatyoudidn’tknowexisted,
”
ordidn’tknowhowtoaskfor,findsyou.
JeffreyM.O’Brien
iii
P
REFACE
First and foremost I would like to thank my supervisor and promotor Antal van den Bosch,
who guided me in my first steps as a researcher, both for my Master’s thesis and my Ph.D.
research. Antal always gave me free reign in investigating many different research prob-
lems, while at the same time managing to steer me in the right direction when the time
calledforit. AntalwasalwaysabletomaketimeformeoranyoftheotherPh.D.students,
andreadandcommentonpaperorpresentationdrafts.
Inadditiontoturningmeintoabetterresearcher,Antalwasalsoinstrumentalinimproving
myGuitarHeroskills. OurthesismeetingsduringyoursabbaticaldoubledasakindofRock
’n Roll Fantasy Camp, where we could both unwind from discussing yet another batch of
experiments I had run or was planning to run. Rock on! Antal also shares my passion for
ice hockey. This resulted in us attending Tilburg Trappers games in Stappegoor as well as
our regular discussions of the latest hockey news. Thanks for inviting me to come see the
NHL All Star games in Breda. Hopefully we will meet again in spirit come May 2010 when
theCanucksbeatthePenguinsintheStanleyCupfinals!
The research presented in this thesis was performed in the context of the À Propos project.
I would like to acknowledge SenterNovem and the Dutch Ministry of Economic Affairs for
funding this project as part of the IOP-MMI program. The À Propos project was started by
Lou Boves, Antal, and Frank Hofstede. I would like to thank Lou and Frank in particular.
Frankwasalwaysabletolookatmyresearchproblemsfromadifferentandmorepractical
angle,andasaresultourdiscussionswerealwaysverystimulating. IwouldalsoliketoMari
Carmen Puerta-Melguizo, Anita Deshpande, and Els den Os, as well as the other members
and attendees of the project meetings for the pleasant cooperation and helpful comments
andsuggestions.
Iwishtothankthemembersofmycommitteefortakingtimeoutoftheirbusyschedulesto
readmydissertationandattendingmydefense: JaapvandenHerik,MaartendeRijke,Lou
Boves,BirgerLarsen,andHansPaijmans. SpecialthanksgotoJaapforhiswillingnesstogo
through my thesis with a fine-grained comb. The readability of the final text has benefited
greatly from his meticulous attention to detail and quality. Any errors remaining in the
thesisaremyown. IwouldalsoliketothankBirgerforhiscomments,whichhelpedtodot
the i’s and cross the t’s of the final product. Finally, I would like to thank Hans Paijmans,
whocontributedconsiderablytomyknowledgeofIR.
iv
Preface v
My Ph.D. years would not have been as enjoyable and successful without my colleagues at
Tilburg University, especially those at the ILK group. It is not everywhere that the bond
between colleagues is as strong as it was in ILK and I will not soon forget the coffee breaks
with the Sulawesi Boys, the BBQs and Guitar Hero parties, lunch runs, after-work drinks,
andthefriendlyandsupportiveatmosphereonthe3rdfloorofDante. Idonothaveenough
room to thank everyone personally here, you know who you are. In your own way, you all
contributedtothisthesis.
Over the course of my Ph.D. I have spent many Fridays at the Science Park in Amsterdam,
working with members of the ILPS group headed by Maarten de Rijke. I would like to
thank Erik Tjong Kim Sang for setting this up and Maarten for allowing me to become a
guest researcher at his group. Much of what I know about doing IR research, I learned
fromthesevisits. FromsmallthingslikevisualizingresearchresultsandLaTeXlayouttoIR
research methodology and a focus on empirical, task-driven research. I hope that some of
what Ihave learned showsin thethesis. I would liketo thank allof the ILPSmembers, but
especially Krisztian, Katja, and Maarten for collaborating with me on expert search, which
hasproventobeaveryfruitfulcollaborationsofar.
I have also had the pleasure of working at the Royal School of Library and Information
ScienceinCopenhagen. IammostgratefultoBirgerLarsenandPeterIngwersen,forhelping
toarrangemyvisitandguidingmearound. ThanksarealsoduetoMette,Haakon,Charles,
Jette, and the other members of the IIIA group for welcoming me and making me feel at
home. Jegglædermigtilatarbejdesammenmedjersnart.
Thanks are due to Sunil Patel for designing part of the stylesheet of this thesis and to
Jonathan Feinberg of http://www.wordle.net/ for the word cloud on the front of this
thesis. I owe Maarten Clements a debt of gratitude for helping me to more efficiently im-
plement his random walk algorithm. And of course thanks to BibSonomy, CiteULike, and
Deliciousformakingtheresearchdescribedinthisthesispossible.
Finally, I would like to thank the three most important groups of people in my life. My
friends,foralwayssupportingmeandtakingmymindoffmywork. Thanksforallthedin-
ners, late-night movies, pool games, talks, vacations and trips we have had so far! Thanks
tomyparentsforalwayssupportingmeandbelievinginme;withoutyouIwouldnothave
been where I am today. Kirstine, thanks for putting up with me while I was distracted by
my work, and thanks for patiently reading and commenting on my Ph.D. thesis. Og tusind
tak fordi du bringer så meget glæde, latter og kærlighed ind i mit liv. Det her er til Timmy
ogOinky!
C
ONTENTS
Preface iv
1 Introduction 1
1.1 SocialBookmarking. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 ScopeoftheThesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 ProblemStatementandResearchQuestions . . . . . . . . . . . . . . . . . . . . . 3
1.4 ResearchMethodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.5 OrganizationoftheThesis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.6 OriginsoftheMaterial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Related Work 9
2.1 RecommenderSystems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.1 CollaborativeFiltering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.2 Content-basedFiltering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.1.3 Knowledge-basedRecommendation . . . . . . . . . . . . . . . . . . . . . 14
2.1.4 RecommendingBookmarks&References . . . . . . . . . . . . . . . . . . 15
2.1.5 RecommendationinContext. . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2 SocialTagging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2.1 Indexingvs. Tagging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.2.2 Broadvs. NarrowFolksonomies. . . . . . . . . . . . . . . . . . . . . . . . 24
2.2.3 TheSocialGraph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.3 SocialBookmarking. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.3.1 Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.3.2 InteractingwithSocialBookmarkingWebsites . . . . . . . . . . . . . . . 28
2.3.3 Researchtasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
I Recommending Bookmarks
3 Building Blocks for the Experiments 35
3.1 RecommenderTasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2 DataSets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.2.1 CiteULike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.2.2 BibSonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
vi
Contents vii
3.2.3 Delicious . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.3 DataRepresentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.4 ExperimentalSetup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.4.1 Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.4.2 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4 Folksonomic Recommendation 55
4.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.2 Popularity-basedRecommendation . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.3 CollaborativeFiltering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.3.1 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.3.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.4 Tag-basedCollaborativeFiltering . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.4.1 TagOverlapSimilarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.4.2 TaggingIntensitySimilarity . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.4.3 SimilarityFusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.5 Relatedwork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.6 ComparisontoRelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
4.6.1 Tag-awareFusionofCollaborativeFilteringAlgorithms . . . . . . . . . 77
4.6.2 ARandomWalkontheSocialGraph. . . . . . . . . . . . . . . . . . . . . 78
4.6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.7 ChapterConclusionsandAnswertoRQ1 . . . . . . . . . . . . . . . . . . . . . . 82
5 Exploiting Metadata for Recommendation 85
5.1 ContextualMetadatainSocialBookmarking . . . . . . . . . . . . . . . . . . . . 86
5.2 ExploitingMetadataforItemRecommendation. . . . . . . . . . . . . . . . . . . 88
5.2.1 Content-basedFiltering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
5.2.2 HybridFiltering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
5.2.3 SimilarityMatching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
5.2.4 SelectingMetadataFieldsforRecommendationRuns . . . . . . . . . . 94
5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.3.1 Content-basedFiltering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.3.2 HybridFiltering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.3.3 ComparisontoFolksonomicRecommendation . . . . . . . . . . . . . . . 98
5.4 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5.4.1 Content-basedFiltering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5.4.2 HybridFiltering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
5.6 ChapterConclusionsandAnswertoRQ2 . . . . . . . . . . . . . . . . . . . . . . 105
6 Combining Recommendations 107
6.1 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
6.1.1 FusingRecommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
Contents viii
6.1.2 DataFusioninMachineLearningandIR . . . . . . . . . . . . . . . . . . 110
6.1.3 WhyDoesFusionWork? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
6.2 FusingRecommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
6.3 SelectingRunsforFusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
6.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
6.4.1 FusionAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
6.4.2 ComparingAllFusionMethods . . . . . . . . . . . . . . . . . . . . . . . . 119
6.5 Discussion&Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
6.6 ChapterConclusionsandAnswertoRQ3 . . . . . . . . . . . . . . . . . . . . . . 121
II Growing Pains: Real-world Issues in Social Bookmarking
7 Spam 125
7.1 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
7.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
7.2.1 DataCollection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
7.2.2 DataRepresentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
7.2.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
7.3 SpamDetectionforSocialBookmarking . . . . . . . . . . . . . . . . . . . . . . . 132
7.3.1 LanguageModelsforSpamDetection . . . . . . . . . . . . . . . . . . . . 133
7.3.2 SpamClassification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
7.3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
7.3.4 DiscussionandConclusions . . . . . . . . . . . . . . . . . . . . . . . . . . 138
7.4 TheInfluenceofSpamonRecommendation. . . . . . . . . . . . . . . . . . . . . 140
7.4.1 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
7.4.2 ExperimentalSetup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
7.4.3 ResultsandAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
7.5 ChapterConclusionsandAnswertoRQ4 . . . . . . . . . . . . . . . . . . . . . . 145
8 Duplicates 147
8.1 DuplicatesinCiteULike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
8.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
8.3 DuplicateDetection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
8.3.1 CreatingaTrainingSet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
8.3.2 ConstructingaDuplicateItemClassifier . . . . . . . . . . . . . . . . . . . 153
8.3.3 ResultsandAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
8.4 TheInfluenceofDuplicatesonRecommendation. . . . . . . . . . . . . . . . . . 160
8.4.1 ExperimentalSetup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
8.4.2 ResultsandAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
8.5 ChapterConclusionsandAnswertoRQ5 . . . . . . . . . . . . . . . . . . . . . . 164
III Conclusion
9 Discussion and Conclusions 169
9.1 AnswerstoResearchQuestions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
9.2 RecommendationsforRecommendation . . . . . . . . . . . . . . . . . . . . . . . 172