Zan Huang
ABSTRACT
Recommender systems can provide valuable services in a digital library environment, as demonstrated by its commercial success in book, movie, and music industries. One of the most commonly-used and successful recommendation algorithms is collaborative filtering, which explores the correlations within user-item interactions to infer user interests and preferences. However, the recommendation quality of collaborative filtering approaches is greatly limited by the data sparsity problem. To alleviate this problem we have previously proposed graph-based algorithms to explore transitive user-item associations. In this paper, we extend the idea of analyzing user-item interactions as graphs and employ link prediction approaches proposed in the recent network modeling literature for making collaborative filtering recommendations. We have adapted a wide range of linkage measures for making recommendations. Our preliminary experimental results based on a book recommendation dataset show that some of these measures achieved significantly better performance than standard collaborative filtering algorithms.
- Adamic, L.A. and Adar, E. Friends and neighbors on the Web. Social Networks, 25, 3 (2003), 211--230.Google Scholar
Cross Ref
- Goldberg, D.S. and Roth, F.P. Assessing experimentally derived interactions in a small world. In Proceedings of the National Academy of Sciences USA, 100, 8 (2003), 4372--4376.Google ScholarCross Ref
- Huang, Z., Chen, H. and Zeng, D. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Information Systems, 22, 1 (2004), 116--142. Google ScholarDigital Library
- Katz, L. A new status index derived from sociometric analysis. Psychometrika, 18, 1 (1953), 39--43.Google ScholarCross Ref
- Liben-Nowell, D. and Kleinberg, J. The link prediction problem for social networks. In Proceedings of the Twelfth International Conference on Information and Knowledge Management, 2003, 556--559. Google ScholarDigital Library
- Popescul, A. and Ungar, L.H. Statistical relational learning for link prediction. In Proceedings of the Workshop on Learning Statistical Models from Relational Data, 2003.Google Scholar
Index Terms
- Link prediction approach to collaborative filtering
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