Abstract
We present a collaborative recommender that uses a user-based model to predict user ratings for specified items. The model comprises summary rating information derived from a hierarchical clustering of the users. We compare our algorithm with several others. We show that its accuracy is good and its coverage is maximal. We also show that the algorithm is very efficient: predictions can be made in time that grows independently of the number of ratings and items and only logarithmically in the number of users.
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References
Breese, J. S., Heckerman, D. & Kadie, C. (1998). Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence, 43–52.
Bridge, D. & Kelleher, J. (2002). Experiments in Sparsity reduction: Using Clustering in Collaborative Recommenders. In O'Neill, M., Sutcliffe, R. F. E., Ryan, C., Eaton, M. & Griffith, N. J. L. (eds.) Proceedings of the Thirteenth Irish Conference on Artificial Intelligence and Cognitive Science, 144–149, Springer.
Chee, S. H. S. (2000). RecTree: A Linear Collaborative Filtering Algorithm. Master's Thesis, Simon Fraser University.
Fisher, D., Hildrum, K., Hong, J., Newman, M. Thomas, M. & Vuduc, R. (2000). SWAMI: A Framework for Collaborative Filtering Algorithm Development and Evaluation. In Proceedings of SIGIR, 366–368.
Herlocker, J. L. (2000). Understanding and Improving Automated Collaborative Filtering Systems. Ph.D. Thesis, University of Minnesota.
Kohrs, A. & Merialdo, B. (1999). Clustering for Collaborative Filtering Applications. In Proceedings of CIMCA99. IOS Press.
O'Connor, M. & Herlocker, J. (1999). Clustering Items for Collaborative Filtering. In Proceedings of the ACM SIGIR Workshop on Recommender Systems.
Sarwar, B. M., Karypis, G., Konstan, J. A. & Riedl, J. (2001). Item-Based Collaborative Filtering Recommendation Algorithms. In Proceedings of the Tenth International World Wide Web Conference, 285–295.
Ungar, L. & Foster, D. (1998). Clustering Methods for Collaborative Filtering. In AAAI Workshop on Recommendation Systems, 112–125.
Wilke, W., Lenz, M. & Wess, S. (1998). Intelligent Sales Support with CBR. In M. L. Lenz et al. (ed.) Case-Based Reasoning Technology: From Foundations to Applications. Springer-Verlag, 91–113.
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Kelleher, J., Bridge, D. An Accurate and Scalable Collaborative Recommender. Artificial Intelligence Review 21, 193–213 (2004). https://doi.org/10.1023/B:AIRE.0000036255.53433.26
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DOI: https://doi.org/10.1023/B:AIRE.0000036255.53433.26