EMUCF: Enhanced multistage user-based collaborative filtering through non-linear similarity for recommendation systems

作者：

Highlights：

• An Enhanced Multistage User-based Collaborative Filtering (EMUCF) algorithm is proposed.

• EMUCF algorithm predicts the user’s unknown rating using Bhat_sim similarity model.

• A hybrid metric is proposed to elicit the dominant users and items for dense matrix.

• Incremental density-based n > 2-stage EMUCF is proposed to increase prediction accuracy.

摘要

•An Enhanced Multistage User-based Collaborative Filtering (EMUCF) algorithm is proposed.•EMUCF algorithm predicts the user’s unknown rating using Bhat_sim similarity model.•A hybrid metric is proposed to elicit the dominant users and items for dense matrix.•Incremental density-based n > 2-stage EMUCF is proposed to increase prediction accuracy.

论文关键词：Recommendation system,Collaborative filtering,Bhattacharyya coefficient,Multistage rating prediction,Non-linear similarity,Entropy maximization

论文评审过程：Received 9 October 2019, Revised 23 May 2020, Accepted 4 July 2020, Available online 12 July 2020, Version of Record 23 July 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113724