基于上下文相似度和社会网络的移动服务推荐方法

doi:10.3969/j.issn.0372-2112.2017.06.036

电子学报 ›› 2017, Vol. 45 ›› Issue (6): 1530-1536.DOI: 10.3969/j.issn.0372-2112.2017.06.036

基于上下文相似度和社会网络的移动服务推荐方法

俞春花¹, 刘学军^1,2, 李斌¹, 章玮³

1. 南京工业大学计算机科学与技术学院, 江苏南京 211816;
2. 复旦大学, 上海 200433;
3. 中国人民解放军 73677部队, 江苏南京 210016

收稿日期:2015-09-15 修回日期:2016-02-03 出版日期:2017-06-25
- 作者简介:
- 俞春花 女,1991年1月出生于江苏省泰州市.现为南京工业大学计算机科学与技术学院硕士研究生.主要研究方向为数据挖掘和个性化推荐.E-mail:chunhuayu1991@163.com;刘学军 男,1970年6月出生于吉林省长春市.博士,现为南京工业大学教授、硕士生导师,CCF高级会员.主要研究方向为数据库、数据挖掘、传感器网络、隐私保护等.E-mail:lxj_njgd@163.com
- 基金资助:
- 国家自然科学基金 (No.61203072）; 江苏省重点研发计划 (社会发展） (No.BE2015697）

Mobile Service Recommendation Based on Context Similarity and Social Network

YU Chun-hua¹, LIU Xue-jun^1,2, LI Bin¹, ZHANG Wei³

1. College of Computer Science and Technology, Nanjing Tech University, Nanjing, Jiangsu 211816, China;
2. Fudan University, Shanghai 200433, China;
3. 73677 PLA Troops, Nanjing, Jiangsu 210016, China

Received:2015-09-15 Revised:2016-02-03 Online:2017-06-25 Published:2017-06-25
- Supported by:
- National Natural Science Foundation of China (No.61203072); Key Research and Development Project of Jiangsu Province (Social Development) (No.BE2015697)

摘要/Abstract

摘要：

针对传统的基于协同过滤的移动服务推荐方法存在的数据稀疏性和用户冷启动问题，提出一种基于上下文相似度和社会网络的移动服务推荐方法（Context-similarity and Social-network based Mobile Service Recommendation，CSMSR）.该方法将基于用户的上下文相似度引入个性化服务推荐过程，并挖掘由移动用户虚拟交互构成的社会关系网络，按照信任度选取信任用户；然后结合基于用户评分相似度计算发现的近邻，分别从相似用户和信任用户中选择相应的邻居用户，对目标用户进行偏好预测和推荐.实验表明，与已有的服务推荐方法TNCF、SRMTC及CF-DNC相比，CSMSR方法有效地缓解数据稀疏性并提高推荐准确率，有利于发现用户感兴趣的服务，提升用户个性化服务体验.

Abstract:

Concentrating on the data sparsity problem and the new user cold-start problem faced by traditional collaborative filtering algorithm in mobile recommender system,an approach named CSMSR (Context-similarity and Social-network based Mobile Service Recommendation) is proposed.The approach integrates mobile users' context information and social network information into collaborative filtering recommendation process.Firstly,it imports the user-based context similarities into the personalized service recommendation process.Secondly,it searches the corresponding nearest neighbors for each mobile user according to the given mobile users' ratings and the mining social network.Finally,it predicts unknown mobile users' preferences and generates recommendations.The experimental results show that CSMSR outperforms the existing service recommendation methods,such as TNCF,SRMTC and CF-DNC in terms of MAE (Mean Absolute Error) and P@N,and it performs well in finding out the interested services of users and enhancing the user experience.

Key words: mobile service recommendation, context, similarity measure, social network, collaborative filtering, data sparsity, cold-start problem

中图分类号:

TP301

俞春花, 刘学军, 李斌, 等. 基于上下文相似度和社会网络的移动服务推荐方法[J]. 电子学报, 2017, 45(6): 1530-1536.

YU Chun-hua, LIU Xue-jun, LI Bin, et al. Mobile Service Recommendation Based on Context Similarity and Social Network[J]. Acta Electronica Sinica, 2017, 45(6): 1530-1536.

参考文献

[1] Jonna H,Albrecht S,Jani M,et al.Context-aware mobile media and social networks[A].Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services[C].Bonn,Germany:IEEE,2009.1-3.
[2] Park M H,Hong J H,Cho S B.Location-Based recommendation system using Bayesian user's preference model in mobile devices[A].Proceedings of the Ubiquitous Intelligence and Computing 2007[C].Berlin:Springer-Verlag,2007.1130-1139.
[3] 高全力,高岭,等.上下文感知推荐系统中基于用户认知行为的偏好获取方法[J].计算机学报,2015,38(9):1767-1776. Gao Quan-li,Gao Ling,et al.A preference elicitation method based on users' cognitive behavior for context-aware recommender system[J].Chinese Journal of Computers,2015,38(9):1767-1776.(in Chinese)
[4] Wang Z,Sun L,Zhu W,et al.Joint social and content recommendation for user-generated videos in online social network[J].IEEE Trans on Multimedia,2013,15(3):698-710.
[5] 王玉祥,乔秀全,等.上下文感知的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 Yu-xiang,Qiao Xiu-quan,et al.Research on context-awareness mobile SNS service selection mechanism[J].Chinese Journal of Computers,2010,33(11):2126-2135.(in Chinese)
[6] Jamali M,Ester M.A matrix factorization technique with trust propagation for recommendation in social networks[A].Proceedings of the ACM Recommender Systems Conf[C].New York:ACM Press,2010.135-142.
[7] Ma H,Zhou D,et al.Recommender systems with social regularization[A].Proceedings of the 4th ACM International Conference on Web Search and Data Mining[C].Hong Kong,China:ACM Press,2011.287-296.
[8] 印桂生,张亚楠,等.基于受限信任关系和概率分解矩阵的推荐[J].电子学报,2014,42(5):904-911. YIN Gui-sheng,ZHANG Ya-nan,et al.A constrained trust recommendation using probabilistic matrix factorization[J].Acta Electronica Sinica,2014,42(5):904-911.(in Chinese)
[9] Breese J S,Heckerman D,et al.Empirical analysis of predictive algorithms for collaborative filtering[A].Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence[C].San Francisco:Morgan Kaufmann Publishers Inc,1998.43-52.
[10] Golbeck J.Computing and Applying Trust in Web-based Social Networks[D].Washington:University of Maryland,College Park,2005.
[11] Eagle N,Pentland A,et al.Inferring social network structure using mobile phone data[J].Proceedings of the National Academy of Sciences (PNAS),2009,106(36):15274-15278.
[12] 王海艳,杨文彬,等.基于可信联盟的服务推荐方法[J].计算机学报,2014,37(2):301-311. Wang Hai-yan,YANG Wen-bin,et al.A service recommendation method based on trustworthy community[J].Chinese Journal of Computers,2014,37(2):301-311.(in Chinese)
[13] 贾冬艳,张付志.基于双重邻居选取策略的协同过滤算法[J].计算机研究与发展,2013,50(5):1076-1084. Jia Dong-yan,Zhang Fu-zhi.A collaborative filtering recommendation algorithm based on double neighbor choosing strategy[J].Journal of Computer Research and Development,2013,50(5):1076-1084.(in Chinese)

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基于上下文相似度和社会网络的移动服务推荐方法

Mobile Service Recommendation Based on Context Similarity and Social Network

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