Toward more diverse recommendations: Item re-ranking methods for recommender systems

Abstract

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 a number of item re-ranking methods that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy. Empirical results consistently show the diversity gains of the proposed re-ranking methods for several real-world rating datasets and different rating prediction techniques.