基于粒子群优化算法的协同过滤推荐并行化研究

doi:10.13190/j.jbupt.2018-028

北京邮电大学学报 ›› 2018, Vol. 41 ›› Issue (6): 115-122.doi: 10.13190/j.jbupt.2018-028

基于粒子群优化算法的协同过滤推荐并行化研究

游思晴, 周丽, 赵东杰, 薛菲

北京物资学院信息学院, 北京 101149

收稿日期:2018-01-26 出版日期:2018-12-28 发布日期:2018-12-24
作者简介:游思晴(1982-),女,讲师,E-mail:93028603@qq.com.
基金资助:
国家自然科学基金项目（71501015）；北京市智能物流系统协同创新中心开放课题

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

摘要/Abstract

摘要： 针对常用协同过滤推荐算法存在计算性能瓶颈的问题，提出了在Spark上并行化实现协同过滤推荐算法RLPSO_KM_CF.首先，通过具备反向学习和局部学习能力的粒子群优化（RLPSO）算法寻找粒子群最优解，输出优化后的聚类中心；然后，运用RLPSO_KM算法对用户信息进行聚类；最后，将传统协同过滤推荐算法与RLPSO_KM聚类结合，从而对目标用户进行有效推荐.实验结果显示，RLPSO_KM_CF算法在推荐准确度方面有显著提高，具有较高的加速比，稳定性也得到了一定提升.

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

中图分类号:

P315.69

游思晴, 周丽, 赵东杰, 薛菲. 基于粒子群优化算法的协同过滤推荐并行化研究[J]. 北京邮电大学学报, 2018, 41(6): 115-122.

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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基于粒子群优化算法的协同过滤推荐并行化研究

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

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