Weighting strategies for a recommender system using item clustering based on genres
作者：
Highlights：
• An original clustering approach for recommender systems.
• The approach is based on item metadata informations (item genres).
• Items are clustered in several clusters.
• Weighting strategies are used to combine cluster’s evaluations.
• MAE is improved between 0.3 and 1.8% and RMSE between 4.7 and 9.8%.
摘要
•An original clustering approach for recommender systems.•The approach is based on item metadata informations (item genres).•Items are clustered in several clusters.•Weighting strategies are used to combine cluster’s evaluations.•MAE is improved between 0.3 and 1.8% and RMSE between 4.7 and 9.8%.
论文关键词：Recommender system,Clustering,Weighting strategies
论文评审过程：Received 29 September 2016, Revised 24 January 2017, Accepted 25 January 2017, Available online 31 January 2017, Version of Record 11 February 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.01.031
