Qi Liu
Enhong Chen
ABSTRACT
In real applications, a given user buys or rates an item based on his/her interests. Learning to leverage this interest information is often critical for recommender systems. However, in existing recommender systems, the information about latent user interests are largely under-explored. To that end, in this paper, we propose an interest expansion strategy via personalized ranking based on the topic model, named iExpand, for building an interest-oriented collaborative filtering framework. The iExpand method introduces a three-layer, user-interest-item, representation scheme, which leads to more interpretable recommendation results and helps the understanding of the interactions among users, items, and user interests. Moreover, iExpand strategically deals with many issues, such as the overspecialization and the cold-start problems. Finally, we evaluate iExpand on benchmark data sets, and experimental results show that iExpand outperforms state-of-the-art methods.
- Movielens datasets. URL: http://www.grouplens.org/node/73#attachments, 2007.Google Scholar
- D. M. Blei, Y. N. Andrew, and I. J. Michael. Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, pages 993--1022, 2003. Google ScholarDigital Library
- F. Fouss, A. Pirotte, J.-M. Renders, and M. Saerens. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans. Knowl. Data Eng., 19(3), pages 355--369, 2007. Google ScholarDigital Library
- S. Funk. Netflix update: Try this at home. URL: http://sifter.org/ simon/journal/20061211.html, 2006.Google Scholar
- M. Gori, and A. Pucci. A random-walk based scoring algorithm applied to recommender engines. In WebKDD'06, pages 127--146, 2006. Google ScholarDigital Library
- T. L. Griffiths and M. Steyvers. Finding scientific topics. In PNAS'04 vol. 101, pages 5228--5235. 2004.Google ScholarCross Ref
- G. Jeh, and J. Widom. Scaling Personalized Web Search. In WWW'03, pages 271--279, 2003. Google ScholarDigital Library
- R. Paul, I. Neophytos, S. Mitesh, B. Peter and R. John. GroupLens: an open architecture for collaborative filtering of netnews. In CSCW'94, pages 175--186, 1994. Google ScholarDigital Library
- H. M. Wallach, I. Murray, R. Salakhutdinov and D. M. Mimno Evaluation methods for topic models. In ICML'09, pages 1105--1112, 2009. Google ScholarDigital Library
- H. M. Wallach. Structured topic models for language. PhD thesis, University of Cambridge, 2008.Google Scholar
- H. Yildirim and M. S. Krishnamoorthy. A random walk method for alleviating the sparsity problem in collaborative filtering. In RecSys'08, pages 131--138, 2008. Google ScholarDigital Library
- C. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen. Improving recommendation lists through topic diversification. In WWW'05, pages 22--32, 2005. Google ScholarDigital Library
Index Terms
- Exploiting user interests for collaborative filtering: interests expansion via personalized ranking
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