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Syst."],"published-print":{"date-parts":[[2017,3,31]]},"abstract":"<jats:p>What makes a good recommendation or good list of recommendations?<\/jats:p>\n          <jats:p>Research into recommender systems has traditionally focused on accuracy, in particular how closely the recommender\u2019s predicted ratings are to the users\u2019 true ratings. However, it has been recognized that other recommendation qualities\u2014such as whether the list of recommendations is diverse and whether it contains novel items\u2014may have a significant impact on the overall quality of a recommender system. Consequently, in recent years, the focus of recommender systems research has shifted to include a wider range of \u201cbeyond accuracy\u201d objectives.<\/jats:p>\n          <jats:p>In this article, we present a survey of the most discussed beyond-accuracy objectives in recommender systems research: diversity, serendipity, novelty, and coverage. We review the definitions of these objectives and corresponding metrics found in the literature. We also review works that propose optimization strategies for these beyond-accuracy objectives. Since the majority of works focus on one specific objective, we find that it is not clear how the different objectives relate to each other.<\/jats:p>\n          <jats:p>Hence, we conduct a set of offline experiments aimed at comparing the performance of different optimization approaches with a view to seeing how they affect objectives other than the ones they are optimizing. We use a set of state-of-the-art recommendation algorithms optimized for recall along with a number of reranking strategies for optimizing the diversity, novelty, and serendipity of the generated recommendations. For each reranking strategy, we measure the effects on the other beyond-accuracy objectives and demonstrate important insights into the correlations between the discussed objectives. For instance, we find that rating-based diversity is positively correlated with novelty, and we demonstrate the positive influence of novelty on recommendation coverage.<\/jats:p>","DOI":"10.1145\/2926720","type":"journal-article","created":{"date-parts":[[2016,12,20]],"date-time":"2016-12-20T13:25:27Z","timestamp":1482240327000},"page":"1-42","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":358,"title":["Diversity, Serendipity, Novelty, and Coverage"],"prefix":"10.1145","volume":"7","author":[{"given":"Marius","family":"Kaminskas","sequence":"first","affiliation":[{"name":"Insight Centre for Data Analytics, University College Cork, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Derek","family":"Bridge","sequence":"additional","affiliation":[{"name":"Insight Centre for Data Analytics, University College Cork, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2016,12,19]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2559952"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 1st International Workshop on Novelty and Diversity in Recommender Systems (DiveRS\u201911)","author":"Adomavicius Gediminas","year":"2011","unstructured":"Gediminas Adomavicius and YoungOk Kwon . 2011 . Maximizing aggregate recommendation diversity: A graph-theoretic approach . In Proceedings of the 1st International Workshop on Novelty and Diversity in Recommender Systems (DiveRS\u201911) . 3--10. Gediminas Adomavicius and YoungOk Kwon. 2011. Maximizing aggregate recommendation diversity: A graph-theoretic approach. In Proceedings of the 1st International Workshop on Novelty and Diversity in Recommender Systems (DiveRS\u201911). 3--10."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2011.15"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1498759.1498766"},{"key":"e_1_2_1_5_1","unstructured":"Chris Anderson. 2006. The Long Tail: Why the Future of Business Is Selling Less of More.   Chris Anderson. 2006. 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