Research on Parallelization of Collaborative Filtering Recommendation Algorithm Based on Particle Swarm Optimization

doi:10.13190/j.jbupt.2018-028

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2018, Vol. 41 ›› Issue (6): 115-122.doi: 10.13190/j.jbupt.2018-028

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Research on Parallelization of Collaborative Filtering Recommendation Algorithm Based on Particle Swarm Optimization

YOU Si-qing, ZHOU Li, ZHAO Dong-jie, XUE Fei

School of Information, Beijing Wuzi University, Beijing 101149, China

Received:2018-01-26 Online:2018-12-28 Published:2018-12-24

Abstract

Abstract: 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.

Key words: collaborative filtering recommendation algorithm, RLPSO algorithm, K-means algorithm, Spark

CLC Number:

P315.69

YOU Si-qing, ZHOU Li, ZHAO Dong-jie, XUE Fei. Research on Parallelization of Collaborative Filtering Recommendation Algorithm Based on Particle Swarm Optimization[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(6): 115-122.

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Research on Parallelization of Collaborative Filtering Recommendation Algorithm Based on Particle Swarm Optimization

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