An Improved Collaborative Filtering Algorithm Based on RLPSO

Abstract

Confront of the large amount of data generated by the Internet and how to make the inherent advantages. The recommendation system is widely used as a means of making effective use of large data and is followed by the people. Collaborative filtering recommendation algorithm cannot avoid the bottleneck of computing performance problems in the recommendation process. In this paper, we propose an improved collaborative filtering recommendation algorithm RLPSO_KM_CF. Firstly, the RLPSO (Reverse-learning and local-learning PSO) algorithm is used to find the optimal solution of particle swarm and output the optimized clustering center. Then, the RLPSO_KM algorithm is used to cluster the user information. Finally, the traditional collaborative filtering algorithm is combined with RLPSO_KM clustering to effectively recommend the target user. The experimental results show that the RLPSO_KM_CF algorithm has a significant improvement in the recommended accuracy and has a higher stability.