SVD-based incremental approaches for recommender systems
作者：
Highlights：
• We propose an incremental algorithm called Incremental ApproSVD.
• It can predict unknown ratings when new items are entering dynamically.
• It is a suboptimal approximation with lower running time.
• We give the upper bound of error generated by Incremental ApproSVD.
• Experiments show the advantages of our algorithm on two real datasets.
摘要
•We propose an incremental algorithm called Incremental ApproSVD.•It can predict unknown ratings when new items are entering dynamically.•It is a suboptimal approximation with lower running time.•We give the upper bound of error generated by Incremental ApproSVD.•Experiments show the advantages of our algorithm on two real datasets.
论文关键词：Singular value decomposition,Incremental algorithm,Recommender system,Experimental evaluation
论文评审过程：Received 16 July 2014, Revised 30 October 2014, Accepted 6 November 2014, Available online 16 December 2014.
论文官网地址：https://doi.org/10.1016/j.jcss.2014.11.016
