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’Informazione University of Siena Via Roma 56, 53100 Siena (ITALY)

Paper Recommending Problem

Too many papers available (web, digital libraries, technical report repositories...)

Finding relevant literature is difficult

Help authors during the filtering process

Personalized ranking of papers

Suggest useful resources to an author

Our Goal

PaperRank Algorithm (iterative)

Iterative equation:

IR PaperRank values vector

C paper correlation matrix

IR i PaperRank value for paper p i

d preference vector depending on good papers

About 20 iterations to converge

PaperRank as a linear operator

Off-line computation

Efficiency issues

Limited number of iterations required

Experimental Results (PaperRank) 1 2 3 4 5 6 7 8 9 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 % nelle prime 10 posizioni 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 (L + )

Experimental Results (PaperRank 2k)

Future Works