| 一种基于信任度的协同过滤推荐方法 |
朱强1,2,孙玉强1,3( ) |
| 2. 浙江传媒学院, 杭州 310018 3. 常州大学, 常州 213000 |
| Collaborative filtering recommender method based on trust |
| Qiang ZHU1,2,Yuqiang SUN1,3() |
| 1. University of Science and Technology of China, Hefei 230022, China 2.Zhejiang University of Media and Communications, Hangzhou 310018, China 3. Changzhou University, Changzhou 213000, China |
摘要:
| |||
| 文章导读 | |||
| 摘要提出了一种基于朋友网数据的推荐方法,包括好友推荐和应用服务推荐。通过分析朋友网中用户之间的好友关系、互动行为等来计算用户之间的信任度,可以获取更真实的社会关系; 根据计算得到的信任度构建用户社会网络,并根据凝聚子群和朋友网中已有的分组信息对构建的社会网络进行社区划分,可以获得更细的社会关系; 根据已获得的社区和用户使用的应用服务,提出了一种朋友网中好友和应用服务的推荐计算方法,在计算应用服务推荐时区分了社区内和社区外的用户与目标用户相似度计算方法的不同,提高了寻找近似邻居的准确率以及服务推荐的准确率。 | |||
| 关键词 ：社会网络划分,信任度,好友推荐,协同过滤 | |||
| Abstract：In this paper, a recommender method based on data was developed, which includes friend recommender and application recommender. According to the friend relationship and interaction behavior, a trust computing method was proposed, with a more authentic social network then constructed with the obtained trust. The cohesive subgroup and existing friend information was employed to divide the constructed social network. Based on the work mentioned above, an improved friend recommender and application method was proposed with the similarity of users in the community being differentiated with the similarity of the user out of the community. The improved method can obtain more nearest neighbors and enhance the accuracy of friend recommender and application recommender. | |||
| Key words：social network divisiontrustfriend recommendercollaborative filtering | |||
| 收稿日期: 2014-01-20 出版日期: 2015-04-16 | |||
| |||
| 基金资助:浙江省自然科学基金资助项目(Y1100314 );国家广电总局研究项目(GD10101) | |||
| 引用本文: |
| 朱强, 孙玉强. 一种基于信任度的协同过滤推荐方法[J]. 清华大学学报（自然科学版）, 2014, 54(3): 360-365. Qiang ZHU, Yuqiang SUN. Collaborative filtering recommender method based on trust. Journal of Tsinghua University(Science and Technology), 2014, 54(3): 360-365. |
| 链接本文: |
| http://jst.tsinghuajournals.com/CN/或 http://jst.tsinghuajournals.com/CN/Y2014/V54/I3/360 |
图表:
 |
| 信任度 |
 |
| 一个简单的社会网络划分的实例 |
 |
| a取不同值时得到的直接好友关系的准确率 |
 |
| β取不同值时得到的间接好友关系的准确率 |
 |
| λ取不同值时预测的应用服务的评分准确率 |
 |
| ρ取不同值时预测的应用服务的评分准确率 |
参考文献:
| [1] | 陈佳, 匡智锋, 李敏. 一种Twitter 社区中社会网络分析方法[J]. 计算机工程, 2012, 38(9): 275-281. CHEN Jia, KUANG Zhifeng, LI Min. Social network analysis method in twitter community[J]. Computer Engineering, 2012, 38(9): 275-281.(in Chinese) |
| [2] | Huang D J. Email-based social network trust [C]// Proc IEEE International Conference on Social Computing / IEEE International Conference on Privacy, Security, Risk and Trust. 2010: 363-370. |
| [3] | Zhou T. Understandingusers' initial trust in mobile banking: An elaboration likelihood perspective[J]. Computers in Human Behavior, 2012, 28: 1518-1525. |
| [4] | Huang D J, Zhou Z B, Hong X Y, et al.Establishing Email-based social network trust for vehicular networks [C]// Proc 7th IEEE Consumer Communications and Networking Conference (CCNC). Las Vegas, NV, 2010: 1-5. |
| [5] | 甘早斌, 丁倩, 李开, 等. 基于声誉的多维度信任计算算法[J]. 软件学报, 2011, 22(10): 2401-2411. GAN Zaobin, DING Qian, LI Kai, et al.Reputation-based multi-dimensional trust algorithm[J]. Journal of Software, 2011, 22(10): 2401-2411.(in Chinese) |
| [6] | 黄武汉, 孟祥武, 王立才. 移动通信网中基于用户社会化关系挖掘的协同过滤算法[J]. 电子与信息学报, 2011, 33(12): 3002-3007. HUANG Wuhan, MENG Xiangwu, WANG Licai. A collaborative filtering algorithm based on users' social relationship mining in mobile communication network[J]. Journal of Electronics & Information Technology, 2011, 33(12): 3002-3007.(in Chinese) |
| [7] | 王玉祥, 乔秀全, 李晓峰, 等. 上下文感知移动社交网络服务选择机制研究[J]. 计算机学报, 2010, 33(11): 2126-2135. WANG Yuxiang, QIAO Xiuquan, LI Xiaofeng, et al.Research on context-awareness mobile SNS service selection mechanism[J]. Chinese Journal of Computers, 2010, 33(11): 2126-2135.(in Chinese) |
| [8] | YuanW W, Guan D H, Lee Y K, et al. The small-world trust network[J]. Appliede Intelligence, 2010, 35(3): 399-410. |
| [9] | 刘建国, 周涛, 郭强. 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): 1-9. LIU Jianguo, 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): 1-9. (in Chinese) |
| [10] | Herlocker J, Konstan J, Terveen L, et a1. Evaluating collaborative filtering recommender systems[J]. ACM Trans Information Systems (TOIS), 2004, 22(1): 20-21. |
相关文章:
|
