A Coverage-Based Approach to Recommendation Diversity On Similarity Graph

Abstract : 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.
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Contributor : Yves Grandvalet <>
Submitted on : Tuesday, October 25, 2016 - 11:28:36 AM
Last modification on : Wednesday, July 4, 2018 - 4:44:02 PM




Shameem Ahamed Puthiya Parambath, Nicolas Usunier, Yves Grandvalet. A Coverage-Based Approach to Recommendation Diversity On Similarity Graph. 10th ACM Conference on Recommender Systems (RecSys '16), Sep 2016, Boston, United States. pp.15--22, ⟨10.1145/2959100.2959149⟩. ⟨hal-01387171⟩



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