Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques

8 years 24 days ago
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
— Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize competition), other important aspects of recommendation quality, such as the diversity of recommendations, have often been overlooked. In this paper, we introduce and explore a number of item ranking techniques that can generate recommendations that have substantially higher aggregate diversity across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating datasets and different rating prediction algorithms.
Gediminas Adomavicius, YoungOk Kwon
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where TKDE
Authors Gediminas Adomavicius, YoungOk Kwon
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