基于社会网络信息流模型的协同过滤算法  

Social network information flow model based collaborative filtering algorithm

作　　者：万里[1,2] 廖建新[1,2] 王纯[1,2] 

机构地区：[1]北京邮电大学网络与交换技术国家重点实验室,北京100876 [2]东信北邮信息技术有限公司,北京100083

出　　处：《吉林大学学报（工学版）》2011年第1期270-275,共6页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家杰出青年科学基金项目(60525110);'973'国家重点基础研究发展规划项目(2007CB307100;2007CB307103);新世纪优秀人才支持计划项目(NCET-04-0111);电子信息产业发展基金项目

摘　　要：为提高个性化推荐技术的准确率,首先在多维半马氏过程的状态空间中定义'空状态',得到扩展多维半马氏过程,将其与社会网络分析理论结合,得到社会网络信息流模型,该模型描述了社会网络成员间的信息流动过程。然后基于社会网络信息流模型,提出协同过滤算法SMRR(Semi-Markov and reward renewal)。实验表明,由于综合考虑用户自身偏好和社会网络中其他成员的影响,SMRR的预测准确率明显高于原有算法。In order to improve the accuracy of personalized recommendation, ＂idle state＂ was firstly introduced into multi-dimension Semi-Markov process, therefore extended multi-dimension Semi- Markov process was obtained. Then by combing the extended multi-dimension Semi-Markov process with social network analysis theory, social network information flow model was established. This model describes the information flow process among social actors. Based on the social network information flow model, a collaborative filtering algorithm, Semi-Markov and Reward Renewal （SMRR）, was proposed. Experiments demonstrate that, regarding accuracy, SMRR performs better than existed algorithms. Considering personalized behavior patterns of actors and the influence of interacting social actors together can improve the accuracy of personalized recommendation.

关 键 词：通信技术 协同过滤 多维半马氏过程 有偿半马氏模型 社会网络 电子商务 

分 类 号：TN915[电子电信—通信与信息系统] TP393[电子电信—信息与通信工程]