Katsuhiko Hayashi
ABSTRACT
This paper studies correlation-based item-item similarity measures for recommendation systems. While current research on recommender systems is directed toward deep learning-based approaches, nearest neighbor methods have been still used extensively in commercial recommender systems due to their simplicity. A crucial step in item-based nearest neighbor methods is to compute similarities between items, which are generally estimated through correlation measures like Pearson. The purpose of this paper is to re-investigate the effectiveness of correlation-based nearest neighbor methods on several benchmark datasets that have been used for recommendation evaluation in recent years. This paper also provides a more effective estimation method for correlation measures than the classical Pearson correlation coefficient and shows that this leads to significant improvements in recommendation performance.
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Index Terms
- Rethinking Correlation-based Item-Item Similarities for Recommender Systems
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