A collaborative filtering recommender system using genetic algorithm
作者：
Highlights：
• Propose a novel recommender system finds the best list of items for users using GA.
• The recommender system generates the recommendation using multi-filtering criteria.
• Propose a multi-objective GA has n-fitness functions based on n-filtering criteria.
• The GA hierarchically evaluates the individuals using three fitness functions.
• Adopt the multi-ratings criteria idea to evaluate the individuals based on n-rates.
• Alleviate cold start and sparsity issues of collaborative filtering.
摘要
•Propose a novel recommender system finds the best list of items for users using GA.•The recommender system generates the recommendation using multi-filtering criteria.•Propose a multi-objective GA has n-fitness functions based on n-filtering criteria.•The GA hierarchically evaluates the individuals using three fitness functions.•Adopt the multi-ratings criteria idea to evaluate the individuals based on n-rates.•Alleviate cold start and sparsity issues of collaborative filtering.
论文关键词：Collaborative filtering,Recommender system,Genetic algorithms,Similarity functions,Hybrid recommender system,Semantic information
论文评审过程：Received 17 October 2019, Revised 19 April 2020, Accepted 19 May 2020, Available online 9 June 2020, Version of Record 9 June 2020.
论文官网地址：https://doi.org/10.1016/j.ipm.2020.102310
