一种优化组合相似度的协同过滤推荐算法

摘要/Abstract

摘要：

为了进一步提高相似度计算的准确性，提出了一种优化组合相似度的协同过滤推荐算法。首先，建立用户项目评分时间矩阵，根据用户对共同评分项目的评分时间先后顺序，计算用户之间的影响力；其次，根据用户对共同评分项目的评分差异，计算评分差异的加权信息熵；最后,将时序行为影响力融入到基于加权信息熵的相似度中，其中融合参数α由随机粒子群优化算法选择。通过与其他相似度计算方法比较，该算法降低了标准平均绝对误差和流行度，在一定程度上降低了数据稀疏性的影响，能更准确地计算相似度，从而提高了推荐质量。

Abstract:

In order to further improve the accuracy of similarity calculation, we propose a collaborative filtering recommendation algorithm to optimize the combination of similarity. Firstly, we establish a matrix of time. According to the time sequence that users score for the items, it can calculate the influence between users. Secondly, according to score differences on the common items, we can calculate the weighted information entropy of score difference. Finally, the influence of temporal behavior is incorporated into the similarity based on weighted information entropy. Fusion parameters therein are selected by the stochastic particle swarm optimization algorithm. In comparison with several other similarity calculation methods, the proposed algorithm reduces the normalized mean absolute error and the popularity. To some extent, it reduces the effect of data sparsity and is more accurate in similarity calculation, thus improving the quality of recommendation.

Key words: collaborative filtering recommendation algorithm, temporal behavior influence, information entropy, particle swarm optimization algorithm

陈曦，成韵姿. 一种优化组合相似度的协同过滤推荐算法[J]. 计算机工程与科学.

CHEN Xi，CHENG Yunzi.

A collaborative filtering recommendation algorithm for

optimizing the combination of similarity

[J]. Computer Engineering & Science.

参考文献［16］

［1］	Ma Hongwei,Zhang Guangwei,Li Peng.Survey of collaborative filtering algorithms［J］.Journal of Chinese Computer Systems,2009,30 (7):12821288.(in Chinese)
［2］	Liu Q,Chen E H,Xiong H,et al.Enhancing collaborative filtering by user interests expansion via personalized ranking［J］.IEEE Transactions on Systems,Man and Cybernetics—B,2012,42(1):218233.
［3］	Jannach D,Zanker M,Felferning A,et al.Recommender systems:An introduction［M］.Jiang Fan,translation.Beijing:Posts & Telcom Press,2013.(in Chinese)
［4］	Su J H,Yeh H H,Yu P S,et al.Music recommendation using content and context information mining［J］.IEEE Intelligent Systems,2010,25(1):1626.
［5］	Leng Yajun,Lu Qing,Liang Changyong.Survey of recommendation based on collaborative filtering［J］.Pattern Recognition and Artificial Intelligence,2014,27(8):720734.(in Chinese)
［6］	Wang Licai, Meng Xiangwu, Zhang Yujie. Contextaware recommender systems［J］.Journal of Software,2012,23(1):120.(in Chinese)
［7］	Meng Xiangwu,Hu Xun,Wang Licai,et al.Mobile recommender systems and their applications［J］.Journal of Software,2013,24(1):91108.(in Chinese)
［8］	Zeng Jianxin,Yang Ning,Xia Peiyong.A collaborative filtering algorithm using improved entropybased similarity measures［J］.Microcomputer Information,2012,28(8):181183.(in Chinese)
［9］	Sun Guangfu,Wu Le,Liu Qi,et al.Recommendations based on collaborative filtering by exploiting sequential behaviors［J］.Journal of Software,2013,24(11):27212733.(in Chinese)
［10］	Liu Hongyan.Social computing:Analysis and excavation of users’ online behavior［M］.Beijing:Tsinghua University Press,2014.(in Chinese)
［11］	Guo Qiang,Liu Jianguo.User’s behavior analysis of online social systems［J］.Complex Systems and Complexity Science,2015,12(2):97102.(in Chinese)
［12］	Meng Xiangwu,Liu Shudong,Zhang Yujie,et al.Research on social recommender systems［J］.Journal of Software,2015,26(6):13561372.(in Chinese)
［13］	Wang Z,Sun L F,Zhu W W,et al.Joint social and content recommendation for usergenerated videos in online social network［J］.IEEE Transactions on Multimedia,2013,15(3):698710.
［14］	QuijanoSanchez L, RecioGarcia J, DiazAgudo B. Social factors in group recommender systems［J］.ACM Transactions on Intelligent Systems and Technology,2013,4(1):11991221.
［15］	Tan Pangning,Steinbach M,Kumar V.Introduction to data mining ［M］.Fan Ming,Fan Hongjian,et al,translation.Beijing:Posts & Telcom Press,2011.(in Chinese)
［16］	Liu Jianguo,Zhou Tao,Guo Qiang,et al.Overview of the evaluated algorithms for the personal recommendation systems［J］.Complex Systems and Complexity Science,2009,6(3):110.(in Chinese)
	附中文参考文献：
［1］	马宏伟,张光卫,李鹏.协同过滤推荐算法综述［J］.小型微型计算机系统,2009,30(7):12821288.
［3］	Jannach D, Zanker M, Felferning A,et al.推荐系统［M］.蒋凡,译.北京:人民邮电出版社,2013.
［5］	冷亚军,陆青,梁昌勇.协同过滤推荐技术综述［J］.模式识别与人工智能,2014,27(8):720734.
［6］	王立才,孟祥武,张玉洁.上下文感知推荐系统［J］.软件学报,2012,23(1):120.
［7］	孟祥武,胡勋,王立才,等.移动推荐系统及其应用［J］.软件学报,2013,24(1):91108.
［8］	曾建新,杨宁,夏培勇.一种基于改进信息熵的协同过滤算法［J］.微计算机信息,2012,28(8):181183.
［9］	孙光福,吴乐,刘淇,等.基于时序行为的协同过滤推荐算法［J］.软件学报,2013,24(11):27212733.
［10］	刘红岩.社会计算:用户在线行为分析与挖掘［M］.北京:清华大学出版社,2014.
［11］	郭强,刘建国.在线社会系统的用户行为分析研究进展［J］.复杂系统与复杂性科学,2015,12(2):97102.
［12］	孟祥武,刘树栋,张玉洁,等.社会化推荐系统研究［J］.软件学报,2015,26(6):13561372.
［15］	Tan Pangning,Steinbach M,Kumar V.数据挖掘导论［M］.范明,范宏建,等译.北京:人民邮电出版社,2011.
［16］	刘建国,周涛,郭强,等.个性化推荐系统评价方法综述［J］.复杂系统与复杂性科学,2009,6(3):110.

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