An efficient and accurate recommendation strategy using degree classification criteria for item-based collaborative filtering

作者：

Highlights：

• An extended classification criteria are proposed to assign items to more classes.

• Hellinger distance based item similarity is proposed to evaluate similarities.

• A sigmoid function is used to emphasize the importance of the co-rated items.

• Results reveal that our algorithm has a favorable efficiency and accuracy.

摘要

•An extended classification criteria are proposed to assign items to more classes.•Hellinger distance based item similarity is proposed to evaluate similarities.•A sigmoid function is used to emphasize the importance of the co-rated items.•Results reveal that our algorithm has a favorable efficiency and accuracy.

论文关键词：Recommender system,Collaborative filtering,Accuracy and efficiency,Hellinger distance (HD),Sigmoid function

论文评审过程：Received 6 November 2019, Revised 10 July 2020, Accepted 12 July 2020, Available online 7 August 2020, Version of Record 12 August 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113756