%0 Journal Article
%A XUE Fei
%A YOU Si-qing
%A ZHAO Dong-jie
%A ZHOU Li
%T Research on Parallelization of Collaborative Filtering Recommendation Algorithm Based on Particle Swarm Optimization
%D 2018
%R 10.13190/j.jbupt.2018-028
%J Journal of Beijing University of Posts and Telecommunications
%P 115-122
%V 41
%N 6
%X In order to solve the computational performance bottleneck of the commonly used collaborative filtering recommendation algorithm, a parallel collaborative filtering recommendation algorithm RLPSO_KM_CF on Spark is proposed. Firstly, the reverse-learning and local-learning particle swarm optimization (RLPSO) algorithm is used to find the optimal solution of the particle swarm and the output clustering center is optimized. Then, the RLPSO_KM algorithm is used to cluster the user information. Finally, the traditional cooperative filtering recommendation algorithm is combined with the RLPSO_KM cluster to effectively recommend the target user. The experimental results show that the improved algorithm has a significant improvement in the recommended accuracy, and has a higher speedup and stability.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2018-028