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Andre Vellino
Andre Vellino
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A Hybrid, Multi-Dimensional Recommender for Journal Articles in a Scientific Digital Library Andre Vellino [email_address] Canada Institute for Scientific and Technical Information National Research Council http://lab.cisti-icist.nrc-cnrc.gc.ca/ WPRS 07 2 November 2007 David Zeber [email_address] Dept. of Statistics Cornell University
Outline of Talk ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction and Motivation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Typical Issues with CF Recommenders ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Specific Issues for Science Digital Libraries ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
General Research Strategy ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommender Citation Seeding ,[object Object],[object Object],[object Object],TechLens approach to Cold Start / Data Sparsity problem
Apply PageRank to Citation Matrix ,[object Object],[object Object],[object Object],[object Object],[object Object],Aurel Constantinescu “Ranking Full-Text Articles using Citation Based Methods” Master’s Thesis, University of Ottawa 47.5 135 87.5 47.5 47.5 87.5 87.5
PageRank-weighted Citation matrix ,[object Object],[object Object],[object Object],[object Object],p 6 p 1 p 5 p 2 p 4 p 3 u 2 p 1 u 1 p 2 p 4 p 3 articles citations p 7 p 8  = constant users     0.3 0.2 0.6 0.3 0.5 0.5 0.7 0.6 0.2 0.4 0.5 0.4
User Project Profiles & IR Modes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Implicit Preferences Generation In Context Search Terms Full Text Author Keyphrase Journal Abstract Project IR Mode Clickstream User State
Multi-Dimensional Ratings Matrix Tom Alice Bob Carol p 1 p 2 p 3 p 4 p 5 p 6 Innovation Information Authority 0.3 0.6 0.3 0.7 0.4 0.7 0.2 G. Adomavicious, R. Sankaranarayanan, S. Sen, A. Tuzhilin, ACM Transactions on Information Systems 2005 Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach 0.7 0.2 0.5
Scaling Strategy: Distributed Recommenders ,[object Object],[object Object],[object Object],[object Object],Distributed Collaborative Filtering with Domain Specialization S. Berkovsky, T.Kuflik, and F. Ricci Proceedings of RecSys2007
UI for Navigating Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Carrot 2 Cluster maps 2D projection of Recommended Item-User Similarity Explanation Clusters Dimensionality weighting slider
Future Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
THANK YOU! Questions? http://lab.cisti-icist.nrc-cnrc.gc.ca /

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Synthese Recommender System

  • 1. A Hybrid, Multi-Dimensional Recommender for Journal Articles in a Scientific Digital Library Andre Vellino [email_address] Canada Institute for Scientific and Technical Information National Research Council http://lab.cisti-icist.nrc-cnrc.gc.ca/ WPRS 07 2 November 2007 David Zeber [email_address] Dept. of Statistics Cornell University
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  • 11. Implicit Preferences Generation In Context Search Terms Full Text Author Keyphrase Journal Abstract Project IR Mode Clickstream User State
  • 12. Multi-Dimensional Ratings Matrix Tom Alice Bob Carol p 1 p 2 p 3 p 4 p 5 p 6 Innovation Information Authority 0.3 0.6 0.3 0.7 0.4 0.7 0.2 G. Adomavicious, R. Sankaranarayanan, S. Sen, A. Tuzhilin, ACM Transactions on Information Systems 2005 Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach 0.7 0.2 0.5
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  • 15. Carrot 2 Cluster maps 2D projection of Recommended Item-User Similarity Explanation Clusters Dimensionality weighting slider
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  • 17. THANK YOU! Questions? http://lab.cisti-icist.nrc-cnrc.gc.ca /

Editor's Notes

  1. Purpose of the talk – to describe an approach to addressing the problem of recommender systems for a DL The system is a work in progress. In collaboration with David Zeber – Ph.D. student in Statistics @ Cornell