Sparseness reduction in collaborative filtering using a nearest neighbour artificial immune system with genetic algorithms
作者：
Highlights：
• Fast learning and data imputation using neighbour artificial immune system with a genetic algorithm.
• Sustained and reliable predictions as the number of missing data increases in the datasets.
• Sustained or improve recommendations of items to users from common methods used in collaborative filtering as missing data increases.
摘要
•Fast learning and data imputation using neighbour artificial immune system with a genetic algorithm.•Sustained and reliable predictions as the number of missing data increases in the datasets.•Sustained or improve recommendations of items to users from common methods used in collaborative filtering as missing data increases.
论文关键词：Collaborative filtering,Genetic algorithms,Recommender systems,Artificial immune systems
论文评审过程：Received 27 May 2018, Revised 3 March 2019, Accepted 16 April 2019, Available online 17 April 2019, Version of Record 8 May 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.04.034
