WIC2006 - Research Paper Recommender Systems: A Random-Walk Based Approach
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Research Paper Recommender Systems: A Random-Walk Based Approach Marco Gori and Augusto Pucci Dipartimento di Ingegneria dell’Informazione University of Siena Via Roma 56, 53100 Siena (ITALY)
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Transcript of WIC2006 - Research Paper Recommender Systems: A Random-Walk Based Approach
Research Paper Recommender Systems: A Random-Walk
Based Approach
Marco Gori and Augusto Pucci
Dipartimento di Ingegneria dell’InformazioneUniversity of Siena
Via Roma 56, 53100 Siena (ITALY)
Paper Recommending ProblemPaper Recommending Problem
Too many papers available (web, digital Too many papers available (web, digital libraries, technical report repositories...)libraries, technical report repositories...)
Finding relevant literature is difficultFinding relevant literature is difficult
Help authors during the filtering processHelp authors during the filtering process
Personalized ranking of papersPersonalized ranking of papers
Suggest Suggest usefuluseful resources to an author resources to an author
Our GoalOur Goal
PaperRank Algorithm (iterative)PaperRank Algorithm (iterative)
Iterative equation:Iterative equation:
IRIR PaperRank values vectorPaperRank values vector
CC paper correlation matrix paper correlation matrix
IRIRii PaperRank value for paper PaperRank value for paper ppii
dd preference vector depending on good papers preference vector depending on good papers
About 20 iterations to convergeAbout 20 iterations to converge
PaperRank as a linear operatorPaperRank as a linear operator
Off-line computationOff-line computation
Efficiency issuesEfficiency issues
Limited number of iterations requiredLimited number of iterations required
Experimental Results (PaperRank)Experimental Results (PaperRank)
1 2 3 4 5 6 7 8 90
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PAPER RANK
Test/Train = 20% / 80%Test/Train = 30% / 70%Test/Train = 40% / 60%Test/Train = 50% / 50%
(1) - Distributed DB 744 nodi(2) - Feature Extraction 621 nodi(3) - HMM 407 nodi(4) - Multiprocessors 901 nodi(5) - Page Rank 654 nodi(6) - Rec. System 699 nodi(7) - Rel. Feedback 984 nodi(8) - Semantic Web 1144 nodi(9) - SVM 463 nodi
Experimental Results (CT)Experimental Results (CT)
1 2 3 4 5 6 7 8 90
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CT
Test/Train = 20% / 80%Test/Train = 30% / 70%Test/Train = 40% / 60%Test/Train = 50% / 50%
(1) - Distributed DB 744 nodi(2) - Feature Extraction 621 nodi(3) - HMM 407 nodi(4) - Multiprocessors 901 nodi(5) - Page Rank 654 nodi(6) - Rec. System 699 nodi(7) - Rel. Feedback 984 nodi(8) - Semantic Web 1144 nodi(9) - SVM 463 nodi
Experimental Results (LExperimental Results (L++))
1 2 3 4 5 6 7 8 90
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L+
Test/Train = 20% / 80%Test/Train = 30% / 70%Test/Train = 40% / 60%Test/Train = 50% / 50%
(1) - Distributed DB 744 nodi(2) - Feature Extraction 621 nodi(3) - HMM 407 nodi(4) - Multiprocessors 901 nodi(5) - Page Rank 654 nodi(6) - Rec. System 699 nodi(7) - Rel. Feedback 984 nodi(8) - Semantic Web 1144 nodi(9) - SVM 463 nodi
Experimental Results (PaperRank 2k)Experimental Results (PaperRank 2k)
1 2 3 4 5 6 7 8 90
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PAPER RANK
Test/Train = 20% / 80%Test/Train = 30% / 70%Test/Train = 40% / 60%Test/Train = 50% / 50%
(1) - Distributed DB 4961 nodi(2) - Feature Extraction 3463 nodi(3) - HMM 3978 nodi(4) - Multiprocessors 4292 nodi(5) - Page Rank 5225 nodi(6) - Rec. System 4367 nodi(7) - Rel. Feedback 6278 nodi(8) - Semantic Web 6423 nodi(9) - SVM 4206 nodi
Future WorksFuture Works
Improving system scalabilityImproving system scalability
Developing a paper recommendation Developing a paper recommendation plug-inplug-in
Negative feedback on papersNegative feedback on papers
Comparison with graph regularization Comparison with graph regularization frameworksframeworks
