Optimization-Based Approaches for Diverse Recommendations
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 project, the researchers introduce and explore a number of optimization-based algorithms that are designed to maximize the diversity of recommendation, while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation will demonstrate the performance of the proposed techniques as well as their computational complexity, using several large-scale real-world rating datasets and different rating prediction algorithms.