A new item similarity based on α-divergence for collaborative filtering in sparse data

作者：

Highlights：

• The α-divergence is discretized to determine item similarity.

• The inherent asymmetry of α-divergence is corrected by designing a new value for α.

• User factor is considered in the design of item similarity.

• The results indicate that the α-CF improves recommendation quality and efficiency.

摘要

•The α-divergence is discretized to determine item similarity.•The inherent asymmetry of α-divergence is corrected by designing a new value for α.•User factor is considered in the design of item similarity.•The results indicate that the α-CF improves recommendation quality and efficiency.

论文关键词：Similarity measure,α-divergence,Collaborative filtering,Recommendation,Management information system

论文评审过程：Received 25 October 2019, Revised 21 July 2020, Accepted 28 September 2020, Available online 13 October 2020, Version of Record 21 October 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.114074