Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques

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Name Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
Technology .net
Category Data Mining
Description 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, 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.
IEEE Paper Yes
IEEE Paper Year 2012

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