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A Coverage-Based Approach to Recommendation Diversity On Similarity Graph

Published: 07 September 2016 Publication History

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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cover image ACM Conferences
RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems
September 2016
490 pages
ISBN:9781450340359
DOI:10.1145/2959100
Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 07 September 2016

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Author Tags

  1. coverage
  2. diverse ranking
  3. diverse recommendation
  4. recommender systems
  5. submodular function

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RecSys '16
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RecSys '16: Tenth ACM Conference on Recommender Systems
September 15 - 19, 2016
Massachusetts, Boston, USA

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RecSys '16 Paper Acceptance Rate 29 of 159 submissions, 18%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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