Singular value decomposition based recommendation using imputed data
作者：
Highlights：
• We propose a novel method (ISVD) to incorporate imputed data into the SVD framework. ISVD also proposes a novel algorithm to choose effective neighbors of users or items for generating imputed data.
• ISVD is useful to all SVD-based recommendation methods.
• We conduct several experiments on four real datasets: MovieLens 100k, MovieLens 1M, Netflix and Filmtrust. Experiment results show that ISVD outperforms the state-of-the-art CFs and the RMSEs/MAEs of ISVD are better than those from other imputation-based and SVD-based methods by more than 10%.
摘要
•We propose a novel method (ISVD) to incorporate imputed data into the SVD framework. ISVD also proposes a novel algorithm to choose effective neighbors of users or items for generating imputed data.•ISVD is useful to all SVD-based recommendation methods.•We conduct several experiments on four real datasets: MovieLens 100k, MovieLens 1M, Netflix and Filmtrust. Experiment results show that ISVD outperforms the state-of-the-art CFs and the RMSEs/MAEs of ISVD are better than those from other imputation-based and SVD-based methods by more than 10%.
论文关键词：Imputation-based recommendation,SVD-based recommendation,Data sparsity
论文评审过程：Received 20 March 2018, Revised 27 August 2018, Accepted 8 September 2018, Available online 12 September 2018, Version of Record 21 November 2018.
论文官网地址：https://doi.org/10.1016/j.knosys.2018.09.011
