Explainable Matrix Factorization for Collaborative Filtering
Author:
Affiliation:
1. University of Louisville, Louisville, KY, USA
Funder
KSEF Award
Publisher
ACM Press
Reference5 articles.
1. J. L. Herlocker, J. A. Konstan, and J. Riedl. Explaining collaborative filtering recommendations. In Proceedings of the 2000 ACM conference on Computer supported cooperative work, pages 241--250. ACM, 2000.
2. J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1):5--53, 2004.
3. K. J\"arvelin and J. Kek\"al\"ainen. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS), 2002.
4. Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, (8):30--37, 2009.
5. N. Tintarev and J. Masthoff. A survey of explanations in recommender systems. In Data Engineering Workshop, 2007 IEEE 23rd International Conference on, pages 801--810. IEEE, 2007.
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