WIC2006 - Research Paper Recommender Systems: A Random-Walk Based Approach

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Every day researchers from all over the world have to filter the huge mass of existing research papers with the crucial aim of finding out useful publications related to their current work. In this paper we propose a research paper recommending algorithm based on the Citation Graph and random-walker properties. The PaperRank algorithm is able to assign a preference score to a set of documents contained in a digital library and linked one each other by bibliographic references. A data set of papers extracted by ACM Portal has been used for testing and very promising performances have been measured.

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WIC2006 - Research Paper Recommender Systems: A Random-Walk Based Approach

  1. 1. 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)
  2. 2. Paper Recommending Problem <ul><li>Too many papers available (web, digital libraries, technical report repositories...) </li></ul><ul><li>Finding relevant literature is difficult </li></ul><ul><li>Help authors during the filtering process </li></ul><ul><li>Personalized ranking of papers </li></ul><ul><li>Suggest useful resources to an author </li></ul>
  3. 3. Our Goal
  4. 4. PaperRank Algorithm (iterative) <ul><li>Iterative equation: </li></ul><ul><li>IR PaperRank values vector </li></ul><ul><li>C paper correlation matrix </li></ul><ul><li>IR i PaperRank value for paper p i </li></ul><ul><li>d preference vector depending on good papers </li></ul><ul><li>About 20 iterations to converge </li></ul>
  5. 5. PaperRank as a linear operator <ul><li>Off-line computation </li></ul><ul><li>Efficiency issues </li></ul><ul><li>Limited number of iterations required </li></ul>
  6. 6. 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
  7. 7. Experimental Results (CT)
  8. 8. Experimental Results (L + )
  9. 9. Experimental Results (PaperRank 2k)
  10. 10. Future Works <ul><li>Improving system scalability </li></ul><ul><li>Developing a paper recommendation plug-in </li></ul><ul><li>Negative feedback on papers </li></ul><ul><li>Comparison with graph regularization frameworks </li></ul>

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