Recommendation Diversification Using Explanations
Cong Yu, Laks V. S. Lakshmanan, Sihem Amer-Yahia · Proceedings - International Conference on Data Engineering · 2009
We introduce the novel notion ofexplanation-baseddiversificationto address the well-known problem of over- specialization in item recommendations.Over-specializationin 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 throughattribute-baseddiversification-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 thereasonforwhichaparticularitemisbeingrecommended(i.e., explanation)-for diversifying the results, without the need to access the attributes of the items. In this paper, we formally define the problem ofexplanation-baseddiversificationand, without going into the details of the actual diversification process, demonstrate its effectiveness on a real world data set, Yahoo! Movies.