A multiobjective genetic algorithm based hybrid recommendation approach - Wang, Pan; Zuo, Xingquan; Guo, Congcong; Li, Ruihong; Zhao, Xinchao; Luo, Chaomin (SSCI : 2017 IEEE Symposium Series on Computational Intelligence : November 27, 2017-December 1, 2017, 2017)
Personalized recommendation approaches have received much attention over the years. In this paper, we propose a hybrid recommendation approach that integrates an item-based collaborative filtering, a user-based collaborative filtering and a matrix factorization method. The approach considers the two objectives of recommendation's accuracy and diversity simultaneously. First, a set of items is created separately by each of the three methods. Then, items produced by the three methods are combined into a set of candidate items. Finally, a multiobjective genetic algorithm is adopted to choose a set of Pareto recommendation lists from the set. Experimental results show that the proposed approach is very effective and is able to produce better Pareto solutions than those comparative approaches.