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山东大学学报（理学版） ›› 2017, Vol. 52 ›› Issue (3): 51-59.doi: 10.6040/j.issn.1671-9352.0.2016.030

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融合社交网络的物质扩散推荐算法

邓小方1,2,钟元生1*,吕琳媛3,王明文4,熊乃学1   

  1. 1. 江西财经大学信息管理学院, 江西 南昌 330013;2. 江西师范大学软件学院, 江西 南昌 330022;3. 杭州师范大学阿里巴巴复杂科学研究中心, 浙江 杭州 310036; 4. 江西师范大学计算机信息工程学院, 江西 南昌 330022
  • 收稿日期:2016-10-11 出版日期:2017-03-20 发布日期:2017-03-20
  • 通讯作者: 钟元生(1968— ),男,博士,教授,博士生导师,研究方向为电子商务信誉管理.E-mail:zhong.ys@163.com E-mail:dxf@jxnu.edu.cn
  • 作者简介:邓小方(1980— ),男,博士研究生,研究方向为复杂网络、推荐算法.E-mail:dxf@jxnu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(71662014,71661015);国家科技支撑计划项目(2015BAH50F02);江西省软科学研究计划重点项目(2016BBA10015);江西省社科规划项目(16GL08,16GL09);江西高校人文与社会科学项目(JC1543)

Mass diffusion on coupled social networks

DENG Xiao-fang1,2, ZHONG Yuan-sheng1*, L(¨overU)Lin-yuan3, WANG Ming-wen4, XIONG Nai-xue1   

  1. 1. College of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, Jiangxi, China;
    2. School of Software, Jiangxi Normal University, Nanchang 330022, Jiangxi, China;
    3. Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 310036, Zhejiang, China;
    4. College of Computer Science, Jiangxi Normal University, Nanchang 330022, Jiangxi, China
  • Received:2016-10-11 Online:2017-03-20 Published:2017-03-20

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摘要： 在互联网信息推荐应用中,恰当地结合用户的社交信息能够进一步提升推荐的精度。 以用户为枢纽节点将社交网络和用户-商品二部图融合为耦合网络,并在此基础上提出了一种基于物质扩散动力过程的推荐算法,该算法将社交网络的朋友信息和用户选择商品的信息进行有机集成,是经典物质扩散算法的一种拓展。 在真实数据集Friendfeed和Epinions上的实验表明,在只计算小度用户的推荐准确率时,该方法比经典的物质扩散算法分别提高了38.48%和9.17%;当测试集所占比例为80%时,对于所有目标用户,算法较经典物质扩散算法的推荐准确率分别提高59.05%和21.62%。 因此,社交网络信息的加入可以显著提高对小度用户的推荐准确度。

关键词: 物质扩散, 社交网络, 推荐算法

Abstract: In the Internet information recommendation application, combining the users social information into recommend systems may further enhance the recommendation accuracy. We transform the social network and the user-commodity bipartite network into a coupled network by considering users as hub nodes and then propose a recommendation algorithm based on the process of mass diffusion dynamics, which integrates the information of friends in the social network and the information of the users selection of items in the user-item bipartite network. It can easily be seen that our approach is an extension of the classical mass diffusion algorithm. Experiments on the real datasets, Friendfeed and Epinions show that the recommendation accuracy of small degree users is improved by 38.48% and 9.17% respectively by comparing our proposed method with the classical mass diffusion algorithm. When the proportion of probe set is 80%, the improvement on recommendation accuracy is 59.05% and 21.62% than that of the classical material diffusion algorithm for all target users. Therefore, the addition of social network information can significantly improve the recommendation accuracy for small degree users.

Key words: mass diffusion, social networks, recommender systems

中图分类号: 

  • TP391
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