Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques

Citations Over TimeTop 1% of 2011 papers

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.

Related Papers

• → Design of Garment Style Recommendation System Based on Interactive Genetic Algorithm(2022)8 cited
• → Weighted hybrid technique for recommender system(2017)18 cited
• → A Scalable Collaborative Filtering Based Recommender System Using Incremental Clustering(2009)17 cited
• → Comparing Pre-filtering and Post-filtering Approach in a Collaborative Contextual Recommender System: An Application to E-Commerce(2009)13 cited
• → Facebook Based Choice Filtering(2017)5 cited