Doctor's Thesis Studies on Improving Retrieval Accuracy in Web Information Retrieval KAZUNARI ...

147 Pages·2012·0.64 MB·English

NAIST-IS-DT0161021
Doctor’s Thesis
Studies on Improving Retrieval Accuracy
in Web Information Retrieval
KAZUNARI SUGIYAMA
February6, 2004
Department ofInformationSystems
GraduateSchoolofInformationScience
Nara Institute ofScienceandTechnology
Doctor’sThesis
submitted toGraduateSchoolofInformationScience,
Nara Institute ofScienceandTechnology
inpartial fulfillmentofthe requirementsforthe degreeof
DOCTORofENGINEERING
KAZUNARISUGIYAMA
Thesiscommittee: ShunsukeUemura,Professor
HiroyukiSeki,Professor
YujiMatsumoto, Professor
MasatoshiYoshikawa,Professor (NagoyaUniversity)
Studies on Improving Retrieval Accuracy
in Web Information Retrieval (cid:3)
KAZUNARI SUGIYAMA
Abstract
With the rapid spread of the Internet, anyone can easily access a variety of infor-
mation on the World Wide Web (WWW). Since information resources on the WWW
continue to grow, it has become increasingly difficult for users to find relevant infor-
mationontheWWW.Inthissituation,Websearchenginesareoneofthemostpopular
methods for finding valuable information effectively. However, users are not satisfied
withtheretrievalaccuracy ofsearchengines. Weconsiderthatthefollowingtwofacts
causethisproblem:
(i) accurateindexingofWebpagesby consideringtheir contentsisnotperformed,
(ii) most search engines cannot provide search results that satisfy each user’s infor-
mationneed.
Theaimofthisthesisistoaddresstheaboveproblemsinordertoimproveretrieval
accuracy in Webinformation retrieval.
In information retrieval systems based on the vector space model, the TF-IDF
scheme is widely used to characterize documents. However, in the case of documents
withhyperlinkstructuressuchasWebpages,itisnecessarytodevelopatechniquefor
representing the contents of Web pages more accurately by exploiting the contents of
their hyperlinked neighboring pages. Therefore, in order to address the problem (i),
we propose several approaches to refining the TF-IDF scheme for a target Web page
byusingthecontentsofitshyperlinkedneighboringpages. Experimentalresultsshow
(cid:3)Doctor’s Thesis, Department of Information Systems, Graduate School of Information Science,
NaraInstituteofScienceandTechnology,NAIST-IS-DT0161021, February6,2004.
i
that,generally,moreaccuratefeature vectorsofatarget Webpagecanbegeneratedin
the case of utilizing the contents of its hyperlinked neighboring pages at levels up to
secondin thebackward directionfromthe target page.
On the other hand, Web search engines help users find useful information on the
WWW. However, when the same query is submitted by different users, typical search
engines return the same result regardless of who submitted the query. Generally,each
userhas differentinformation needs for his/her query. Hence, the search result should
beadaptedtouserswithdifferentinformationneeds. Therefore,toaddresstheproblem
(ii),we proposeseveralapproachesto adaptingsearchresults accordingto each user’s
needforrelevantinformation. Experimentalresultsshowthatmorefine-grainedsearch
systems that adapt to user’s preferences can be achieved by constructing user profiles
basedon modifiedcollaborativefiltering.
Webelievethat,inthefieldofWebinformationretrieval,ourproposedapproaches
contribute for indexing a target Web page more accurately, and allowing each user to
performmorefine-grainedsearch thatsatisfyhis/herinformationneed.
Keywords:
WWW, information retrieval, hyperlink, TF-IDF scheme, information filtering, rele-
vancefeedback,usermodeling
ii
Acknowledgements
I am most grateful to Professor Shunsuke Uemura for giving me a chance to study
Information Retrieval in Database Labolatory. He not only gave me lots of advices
derived from his vast range of knowledge but also often encouraged me. I am also
grateful to Professor Masatoshi Yoshikawa at Nagoya University, Associate Professor
Jun Miyazaki, Assistant Professor Kenji Hatano and Assistant Professor Toshiyuki
Amagasaforgiving me alotofcommentsandadvices.
IsincerelythankthemembersofthethesiscommitteeincludingProfessorHiroyuki
Seki and Professor Yuji Matsumoto for reviewing this thesis. They gave me useful
commentswhich Ihadnot noticed.
I would like to thank Professor Hiroshi Nakagawa (he is currently at The Uni-
versity of Tokyo), Associate Professor Tatsunori Mori and Dr. Koichi Yamada (he is
currently at Tokyo Denki University) at Yokohama National University where I had
studied in undergraduate and master course days. They gave me informative knowl-
edge about information retrieval, natural language processing, image processing, and
machinelearning.
I appreciate Professor Shlomo Moran at Technion (Israel Institute of Technology)
who gave me valuable comments at my poster presentation in VLDB2002. I also
appreciate Mr. Chen Yongjun, FUJITSU Corporation, who asked me questions at
DEXA2002inFrance. Itwasquiteunexpectedthat,inFrance,apersonwhoworksfor
aJapanesecompanyaskedmequestions. IgratefullyappreciateDr. KrishnaBharatat
Google who was my mentor of SIGIR2003. Due to a lot of his suggestive and valu-
able comments, I could refine the quality of my paper although it was not accepted at
SIGIR2003. Furthermore,atHyperText’03,IsincerelyappreciateDr. KevinMcCurley
at IBM Almaden Research Center who gave me valuable comments for my research,
Professor Andruid Kerne at Texas A&M University who highly praised my research
aftermypresentation,andconferencecommitteeswhonominatedmypaperforoneof
the Ted NelsonNewcomer Awardcandidates. Iwould liketo acknowledgethe contri-
bution of Dr. Ayse Goker at Robert Gordon University in reviewing my paper as my
mentorofSIGIR2004. ShegavemealotofinformativeadvicethatIhadnotnoticed. I
would also like to express my sincere appreciation to anonymousreviewers of several
conferences. Icould evolvemystudyduetoalot oftheir helpfulcomments.
I also thank former andcurrent members in DatabaseLabolatory. Discussion with
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themprovidedme withnewandinnovativeideasformyresearch.
I thank my best friends, Keisuke Fujimori at KAJIMA Corporation, Kunihiko Fu-
tami at Olympus Corporation, Masaki Kobayashi at Seiko Epson Corporation, and
YoshihisaShimizuatNipponOilCorporation. Theyencouragedmewheneverwemet
together. Thanks also go to the members of swimming club in my high school days.
They always have great zeal in obtaining their own goal. Their such attitudes always
inspiremeto greaterefforts.
Finally, I thank my family for their support and encouragement throughout my
doctoralcoursedays atNara InstituteofScienceandTechnology.
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Contents
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
1. Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 OrganizationofthisThesis . . . . . . . . . . . . . . . . . . . . . . . 3
2. BasicTechniquesinInformationRetrieval 5
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 ExtractionofIndexTerms . . . . . . . . . . . . . . . . . . . . . . . 7
2.2.1 DocumentPreprocessing . . . . . . . . . . . . . . . . . . . . 8
2.2.2 LexicalAnalysisoftheText . . . . . . . . . . . . . . . . . . 8
2.2.3 Elimination ofStopwords . . . . . . . . . . . . . . . . . . . 9
2.2.4 Stemming . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3 RetrievalModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.1 BooleanModel . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3.2 VectorSpaceModel . . . . . . . . . . . . . . . . . . . . . . 14
2.3.3 ProbabilisticModel . . . . . . . . . . . . . . . . . . . . . . . 18
2.4 RetrievalEvaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.4.1 Recall andPrecision . . . . . . . . . . . . . . . . . . . . . . 22
2.4.2 AlternativeMeasures . . . . . . . . . . . . . . . . . . . . . . 30
2.5 Related TechniquesofInformationRetrieval . . . . . . . . . . . . . . 31
2.5.1 RelevanceFeedback . . . . . . . . . . . . . . . . . . . . . . 31
2.5.2 InformationExtraction . . . . . . . . . . . . . . . . . . . . . 34
2.5.3 InformationFiltering . . . . . . . . . . . . . . . . . . . . . . 39
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3. Related Work 43
3.1 Related Workon WebSearchBasedon itsHyperlinkStructures . . . 43
3.1.1 InformationRetrievalSystemsbasedonthe Conceptof
“OptimalDocumentGranularity” . . . . . . . . . . . . . . . 43
3.1.2 HITSAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . 44
3.1.3 PageRankAlgorithm . . . . . . . . . . . . . . . . . . . . . . 45
3.2 Related Workon WebSearchBasedon User’sPreferences . . . . . . 46
3.2.1 Hyperlink-BasedPersonalizedWebSearch . . . . . . . . . . 46
3.2.2 PersonalizedWebSites . . . . . . . . . . . . . . . . . . . . . 47
3.2.3 RecommenderSystems . . . . . . . . . . . . . . . . . . . . . 49
3.2.4 PersonalizedMultimediaSystems . . . . . . . . . . . . . . . 52
4. WebSearch Based onitsHyperlink Structures 55
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.2 ProposedMethods . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2.1 MethodI . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.2.2 MethodII . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.2.3 MethodIII . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.3.1 ExperimentalSetup . . . . . . . . . . . . . . . . . . . . . . . 70
4.3.2 ExperimentalResults . . . . . . . . . . . . . . . . . . . . . . 72
4.3.3 ExperimentsforFurtherImprovement . . . . . . . . . . . . . 78
4.3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.4 ConclusionofthisChapter . . . . . . . . . . . . . . . . . . . . . . . 85
5. WebSearch Based onUser’s Preferences 87
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
5.2 ProposedMethods . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
5.2.1 UserProfile ConstructionBased onPure BrowsingHistory . . 91
5.2.2 User Profile Construction Based on Modified Collaborative
FilteringAlgorithm . . . . . . . . . . . . . . . . . . . . . . . 93
5.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
5.3.1 ExperimentalSetup . . . . . . . . . . . . . . . . . . . . . . . 98
5.3.2 ExperimentalResults . . . . . . . . . . . . . . . . . . . . . . 100
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5.3.3 ExperimentsforFurtherImprovement . . . . . . . . . . . . . 106
5.3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5.4 ConclusionofthisChapter . . . . . . . . . . . . . . . . . . . . . . . 114
6. Conclusions 115
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
A ClusteringAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 131
A.1 K-MeansClustering . . . . . . . . . . . . . . . . . . . . . . 131
A.2 K-Nearest NeighborClustering . . . . . . . . . . . . . . . . 132
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List of Figures
2.1 Ageneralmodelofinformationretrieval. . . . . . . . . . . . . . . . 6
2.2 Thethreeconjunctivecomponentsforthe query[Q = t ^(:t _t )]. 13
a b c
2.3 Definitionofthe Booleanmodel. . . . . . . . . . . . . . . . . . . . . 13
2.4 Definitionofthe vectorspacemodel. . . . . . . . . . . . . . . . . . . 16
2.5 VectorSpaceModel. . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.6 Definitionofthe probabilistic model. . . . . . . . . . . . . . . . . . . 19
2.7 Recall andprecisionforagiven exampleinformationrequest. . . . . . 23
2.8 Precisionat11standardrecalllevels. . . . . . . . . . . . . . . . . . . 25
2.9 Recall andprecisionforagiven exampleinformationrequest. . . . . . 27
2.10 Aprecisionhistogram. . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.11 InformationExtractiontransformingatext’sstructures. . . . . . . . . 38
2.12 Typical information extractionsystemcomponents. . . . . . . . . . . 38
2.13 Ageneralmodelofinformationfiltering. . . . . . . . . . . . . . . . . 40
4.1 The refinement of a feature vector as performed by Method I [(a) in
the Webspace,(b)in thevectorspace]. . . . . . . . . . . . . . . . . 61
4.2 The refinement of a feature vector as performed by Method II [(a) in
the Webspace,(b)in thevectorspace]. . . . . . . . . . . . . . . . . 65
4.3 The refinement of a feature vector as performed by Method III [(a) in
the Webspace,(b)in thevectorspace]. . . . . . . . . . . . . . . . . 69
4.4 AverageprecisionbasedonMethodI–i. . . . . . . . . . . . . . . . . 73
4.5 AverageprecisionbasedonMethodI–ii. . . . . . . . . . . . . . . . . 73
4.6 Distribution ofaveragesimilarity. . . . . . . . . . . . . . . . . . . . 73
4.7 AverageprecisionbasedonMethodII–i(a). . . . . . . . . . . . . . . 74
4.8 AverageprecisionbasedonMethodII–i(b). . . . . . . . . . . . . . . 74
4.9 AverageprecisionbasedonMethodII–i(c). . . . . . . . . . . . . . . 74
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