Journal
DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, DASFAA 2016
Volume 9645, Issue -, Pages 15-26Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-319-32055-7_2
Keywords
Recommender system; Collaborative filtering; Diversity; Two-step recommendation; Popularity normalization
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Recommender systems become increasingly significant in solving the information overload problem. Beyond conventional rating prediction and ranking prediction recommendation technologies, two-step recommendation algorithms have been demonstrated that they have outstanding accuracy performance in top-N recommendation tasks. However, their recommendation lists are biased towards popular items. In this paper, we propose a popularity normalization method to improve the diversity of user-based two-step recommendation algorithms. Experiment results show that our proposed approach improves the diversity performance significantly while maintaining the advantage of two-step recommendation approaches on accuracy metrics.
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