%0 Conference Paper %1 citeulike:14139122 %A Puthiya Parambath, Shameem A. %A Usunier, Nicolas %A Grandvalet, Yves %B Proceedings of the 10th ACM Conference on Recommender Systems %C New York, NY, USA %D 2016 %I ACM %K diversity recommender recsys2016 similarity %P 15--22 %R 10.1145/2959100.2959149 %T A Coverage-Based Approach to Recommendation Diversity On Similarity Graph %U http://dx.doi.org/10.1145/2959100.2959149 %X 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. %@ 978-1-4503-4035-9

@inproceedings{citeulike:14139122, 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.}}, added-at = {2018-03-19T12:24:51.000+0100}, address = {New York, NY, USA}, author = {Puthiya Parambath, Shameem A. and Usunier, Nicolas and Grandvalet, Yves}, biburl = {https://www.bibsonomy.org/bibtex/29b73965e61720002beac339d8e6d28f8/aho}, booktitle = {Proceedings of the 10th ACM Conference on Recommender Systems}, citeulike-article-id = {14139122}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=2959149}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/2959100.2959149}, doi = {10.1145/2959100.2959149}, interhash = {c6d35a8ac796f99bd2d41b69e5099923}, intrahash = {9b73965e61720002beac339d8e6d28f8}, isbn = {978-1-4503-4035-9}, keywords = {diversity recommender recsys2016 similarity}, location = {Boston, Massachusetts, USA}, pages = {15--22}, posted-at = {2016-09-17 16:17:12}, priority = {2}, publisher = {ACM}, series = {RecSys '16}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{A Coverage-Based Approach to Recommendation Diversity On Similarity Graph}}, url = {http://dx.doi.org/10.1145/2959100.2959149}, year = 2016 }