It takes variety to make a world: diversification in recommender systems
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Diversification and refinement in collaborative filtering recommender
Proceedings of the 20th ACM international conference on Information and knowledge management
Ranking objects by following paths in entity-relationship graphs
Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management
Online selection of diverse results
Proceedings of the fifth ACM international conference on Web search and data mining
Proceedings of the twenty-ninth annual symposium on Computational geometry
Diversity maximization under matroid constraints
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Real-time recommendation of diverse related articles
Proceedings of the 22nd international conference on World Wide Web
From query to question in one click: suggesting synthetic questions to searchers
Proceedings of the 22nd international conference on World Wide Web
Proceedings of the 22nd international conference on World Wide Web
On the complexity of query result diversification
Proceedings of the VLDB Endowment
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We introduce the novel notion of {\em explanation-based diversification} to address the well-known problem of {\em over-specialization} 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 {\em 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 {\em reason for which a particular item is being recommended} (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 of {\em explanation-based diversification} and, without going into the details of the actual diversification process, demonstrate its effectiveness on a real world data set, Yahoo!~Movies.