A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques
作者：
Highlights：
• A new method is developed for recommender systems.
• The recommender system is developed based on collaborative filtering.
• Scalability and sparsity issues in recommender systems are solved.
• MovieLens and Yahoo! Webscope R4 datasets are used for method evaluation.
• The method is effective in solving the sparsity and scalability problems in CF.
摘要
•A new method is developed for recommender systems.•The recommender system is developed based on collaborative filtering.•Scalability and sparsity issues in recommender systems are solved.•MovieLens and Yahoo! Webscope R4 datasets are used for method evaluation.•The method is effective in solving the sparsity and scalability problems in CF.
论文关键词：Recommender systems,Ontology,Clustering,Dimensionality reduction,Scalability,Sparsity
论文评审过程：Received 22 July 2017, Revised 23 September 2017, Accepted 24 September 2017, Available online 29 September 2017, Version of Record 9 October 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.09.058
