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 and explore a number of item ranking techniques that can generate substantially more diverse recommendations 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 data sets and different rating prediction algorithms.
Original language | English (US) |
---|---|
Article number | 5680904 |
Pages (from-to) | 896-911 |
Number of pages | 16 |
Journal | IEEE Transactions on Knowledge and Data Engineering |
Volume | 24 |
Issue number | 5 |
DOIs | |
State | Published - 2012 |
Bibliographical note
Funding Information:The research reported in this paper was supported in part by the US National Science Foundation (NSF) grant IIS-0546443.
Keywords
- Recommender systems
- collaborative filtering.
- performance evaluation metrics
- ranking functions
- recommendation diversity