A Coverage-Based Approach to Recommendation Diversity On Similarity Graph

Puthiya Parambath, S. A. , Usunier, N. and Grandvalet, Y. (2016) A Coverage-Based Approach to Recommendation Diversity On Similarity Graph. In: Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16), Boston, MA, USA, 15-19 Sept 2016, pp. 15-22. ISBN 9781450340359 (doi:10.1145/2959100.2959149)

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We consider the problem of generating diverse, personalized recommendations such that a small set of recommended items covers a broad range of the user's interests. We represent items in a similarity graph, and we formulate the relevance/diversity trade-off as finding a small set of unrated items that best covers a subset of items positively rated by the user. In contrast to previous approaches, our method does not rely on an explicit trade-off between a relevance objective and a diversity objective, as the estimations of relevance and diversity are implicit in the coverage criterion. We show on several benchmark datasets that our approach compares favorably to the state-of-the-art diversification methods according to various relevance and diversity measures.

Item Type:Conference Proceedings
Additional Information:This work was carried out and funded in the framework of the Labex MS2T. It was supported by the Picardy Re- gion and the French Government, through the program\In- vestments for the future" managed by the National Agency for Research (Reference ANR-11-IDEX-0004-02). We thank our anonymous reviewers for their valuable suggestions and comments.
Glasgow Author(s) Enlighten ID:Puthiya Parambath, Dr Sham
Authors: Puthiya Parambath, S. A., Usunier, N., and Grandvalet, Y.
College/School:College of Science and Engineering > School of Computing Science

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