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 del.icio.us. 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...

• Type: Conference
• Year: 2009
• Where: ICDE
• Authors: Cong Yu, Laks V. S. Lakshmanan, Sihem Amer-Yahia
• Keywords: Actual Diversification Process, Attribute-based Diversification--grouping Items, Database, Explanation-based Diversification, Explanationbased Diversification, ICDE 2009, Social Content Recommendations