A multi-level collaborative filtering method that improves recommendations

作者：

Highlights：

• We propose a recommendation method that improves collaborative filtering.

• We divide the Pearson Correlation Similarity (PCC) in multiple levels.

• The proposed method has been tested on five real datasets.

• A comparison to alternative methods is provided in order to show its effectiveness.

摘要

•We propose a recommendation method that improves collaborative filtering.•We divide the Pearson Correlation Similarity (PCC) in multiple levels.•The proposed method has been tested on five real datasets.•A comparison to alternative methods is provided in order to show its effectiveness.

论文关键词：Collaborative filtering,Similarity,Multi-level,Hybrid,Recommender system

论文评审过程：Received 28 July 2015, Revised 25 November 2015, Accepted 26 November 2015, Available online 4 December 2015, Version of Record 22 December 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.11.023