LsRec: Large-scale social recommendation with online update
作者：
Highlights：
• Social influence could capture the intricate relations between users.
• Item clustering could reflect user’s preference.
• Recommendation within each item cluster achieves high performance.
• The incremental update strategy guarantees flexible online scalability.
• Computational cost decreases with online incremental update.
摘要
•Social influence could capture the intricate relations between users.•Item clustering could reflect user’s preference.•Recommendation within each item cluster achieves high performance.•The incremental update strategy guarantees flexible online scalability.•Computational cost decreases with online incremental update.
论文关键词：Social recommendation,Online update,Item clustering,Matrix factorization
论文评审过程：Received 10 May 2019, Revised 10 June 2020, Accepted 9 July 2020, Available online 26 July 2020, Version of Record 30 July 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113739
