Recommendation Diversification Using Explanations

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Recommendation Diversification Using Explanations
Abstract-- We introduce the novel notion of explanationbased diversification to address the well-known problem of overspecialization in item recommendations. Over-specialization in recommender systems leads to result sets with items that are too similar to one another, thus reducing the diversity of results and limiting user choices. Traditionally, the problem is addressed through attribute-based diversification--grouping items in the result set that share many common attributes (e.g., genre for movies) and selecting only a limited number of items from each group. It is, however, not always applicable, especially for social content recommendations. For example, attributes may not be available as in the case of recommending URLs for users of Explanation-based diversification provides a novel and complementary alternative--it leverages the reason for which a particular item is being recommended (i.e., explanation)--for diversifying the results, without the need to access the...
Cong Yu, Laks V. S. Lakshmanan, Sihem Amer-Yahia
Added 20 Oct 2009
Updated 20 Oct 2009
Type Conference
Year 2009
Where ICDE
Authors Cong Yu, Laks V. S. Lakshmanan, Sihem Amer-Yahia
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