Improving recommender systems via a Dual Training Error based Correction approach

作者：

Highlights：

• DTEC takes into account the error in the training set between users and items.

• A dual system efficiently combines the user and item view point corrections.

• Improvement of recommendations of the SCoR system.

• Applicability of DTEC on other recommender systems to improve recommendations.

• High performance results on four well-known, real world datasets.

摘要

•DTEC takes into account the error in the training set between users and items.•A dual system efficiently combines the user and item view point corrections.•Improvement of recommendations of the SCoR system.•Applicability of DTEC on other recommender systems to improve recommendations.•High performance results on four well-known, real world datasets.

论文关键词：Recommender system,Collaborative filtering,Matrix factorization,User/item similarity,Synthetic coordinates

论文评审过程：Received 22 May 2020, Revised 7 June 2021, Accepted 7 June 2021, Available online 11 June 2021, Version of Record 15 June 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115386