Diversity-Conscious Retrieval

David McSherry

School of Information and Software Engineering, University
of Ulster, Coleraine BT52 1SA, Northern Ireland
dmg.mcsherry@ulst.ac.uk



Abstract. There is growing awareness of the need for recommender systems to
offer a more diverse choice of alternatives than is possible by simply retrieving
the cases that are most similar to a target query. Recent research has shown that
major gains in recommendation diversity can often be achieved at the expense
of relatively small reductions in similarity. However, there are many domains
in which it may not be acceptable to sacrifice similarity in the interest of
diversity. To address this problem, we examine the conditions in which
similarity can be increased without loss of diversity and present a new
approach to retrieval which is designed to deliver such similarity-preserving
increases in diversity when possible. We also present a more widely applicable
approach to increasing diversity in which the requirement that similarity is
fully preserved is relaxed to allow some loss of similarity, provided it is strictly
controlled.
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