Improving matrix factorization recommendations for examples in cold start

作者：

Highlights：

• Novel framework for the imputation of missing values into the ratings matrix.

• Imputation of missing values significantly reduces matrix factorization prediction error.

• Increased matrix factorization performance in the cold start state.

摘要

•Novel framework for the imputation of missing values into the ratings matrix.•Imputation of missing values significantly reduces matrix factorization prediction error.•Increased matrix factorization performance in the cold start state.

论文关键词：Recommender systems,Cold start,Matrix factorization,Imputation,Missing values

论文评审过程：Available online 4 May 2015, Version of Record 26 May 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.04.071