Simultaneous incremental matrix factorization for streaming recommender systems

作者：

Highlights：

• A novel method for modeling multiple data streams (SIMF) was developed.

• Implemented as real-time recommender system based on collective matrix factorization.

• Results on synthetic and real streams confirm that data fusion improves predictions.

• Cold-start problem can be alleviated using SIMF and additional data streams.

摘要

•A novel method for modeling multiple data streams (SIMF) was developed.•Implemented as real-time recommender system based on collective matrix factorization.•Results on synthetic and real streams confirm that data fusion improves predictions.•Cold-start problem can be alleviated using SIMF and additional data streams.

论文关键词：Recommender systems,Data fusion,Matrix factorization,Data streams,Incremental learning

论文评审过程：Received 13 September 2019, Revised 3 February 2020, Accepted 19 June 2020, Available online 29 June 2020, Version of Record 20 July 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113685