基于声誉计算的可信O2O服务提供商推荐方法

doi:10.3969/j.issn.0255-8297.2017.05.005

应用科学学报 ›› 2017, Vol. 35 ›› Issue (5): 585-601.doi: 10.3969/j.issn.0255-8297.2017.05.005

• 2016中国计算机应用大会遴选论文 • 上一篇下一篇

基于声誉计算的可信O2O服务提供商推荐方法

朱文强, 钟元生

江西财经大学软件与通信工程学院, 南昌 330013

收稿日期:2016-10-03 修回日期:2016-12-02 出版日期:2017-09-30 发布日期:2017-09-30
作者简介:朱文强,博士生,讲师,研究方向:推荐系统、服务计算、信任管理、移动学习等,E-mail:stbrook@aliyun.com;钟元生,教授,博导,研究方向:电子商务、信任管理、教育技术、移动学习等,E-mail:zhong_ys@jxufe.edu.cn
基金资助:
国家自然科学基金（No.71662014，No.71361012，No.61462030，No.61602219）；江西省自然科学基金（No.20132BAB201050）资助

Trustworthiness Recommendation for O2O Service Providers Based on Their Reputation

ZHU Wen-qiang, ZHONG Yuan-sheng

School of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang 330013, China

Received:2016-10-03 Revised:2016-12-02 Online:2017-09-30 Published:2017-09-30

摘要/Abstract

摘要：

O2O电子商务近年来发展迅猛，然而部分O2O服务提供商存在不能诚信经营的问题.面对浩如烟海的O2O服务提供商，用户难以做出对自己有利的选择.针对这一问题，提出一种基于声誉计算的可信O2O服务提供商推荐方法.该方法根据用户服务评价矩阵计算用户对服务的加权评价，产生用户对商家的综合评价，进而得到用户商家综合评价矩阵.结合商家声誉，计算用户之间的O2O服务提供商偏好相似性，然后进行推荐.与现有的推荐方法相比，所提出的推荐方法能更有效地对O2O服务提供商的服务质量和经营是否诚信等问题进行过滤，同时考虑用户的偏好属性，具有更高的推荐准确度和更强的抗欺诈能力.

Abstract:

As online to ofine (O2O) e-commerce develops quickly in recent years, many problems have occurred. Some service providers are poor, while some providers are dishonest. It is difcult for users to choose an honest O2O service provider who can provide suitable service. To solve the problem, we propose a method for trustworthiness recommendation of suitable O2O service providers to users based on the providers' reputation and users' similarity. A user-service rating matrix is used to compute the users' comprehensive ratings on O2O service providers, and a comprehensive user-service provider rating matrix generated. The recommendation method combines the matrix with reputations of the O2O service providers to compute similarities of different users. Suitable O2O service providers are then recommended to users. Simulation and experimental results demonstrate that the proposed method has better recommendation accuracy as compared to other traditional methods, as well as some state-of-the-art methods. It performs well in resisting malicious attacks.

Key words: recommender system, online to ofine (O2O)e-commerce, trustworthy O2O service provider, reputation, service recommendation, collaborative fltering

中图分类号:

TP399

朱文强, 钟元生. 基于声誉计算的可信O2O服务提供商推荐方法[J]. 应用科学学报, 2017, 35(5): 585-601.

ZHU Wen-qiang, ZHONG Yuan-sheng. Trustworthiness Recommendation for O2O Service Providers Based on Their Reputation[J]. Journal of Applied Sciences, 2017, 35(5): 585-601.

参考文献

[1] Iresearch. The O2O industry research reports on china[R]. Beijing:Research Report, 2015.
[2] Verbert K, Manouselis N, Ochoa X. Context-aware recommender systems for learning:a survey and future challenges[J]. IEEE Transactions on Learning Technologies, 2012, 5(4):318-335.
[3] Woerndl W, Brocco M, Eigner R. Context-aware recommender systems in mobile scenarios[J]. International Journal of Information Technology & Web Engineering, 2009, 4(1):67-85.
[4] Liu X, Liu Y, Aberer K. Personalized point-of-interest recommendation by mining users' preference transition[C]//Proceedings of the 22nd ACM International Conference on Information & Knowledge Management(CIKM 2013). Burlingame, USA, 2013:733-738.
[5] 孔栋,左美云,孙凯. O2O模式分类体系构建的多案例研究[J]. 管理学报,2015, 12(11):1588-1597. Kong D, Zuo M Y, Sun K. Building O2O model taxonomic system:a multi-case study[J]. Chinese Journal of Management, 2015, 12(11):1588-1597. (in Chinese)
[6] 刘树栋,孟祥武. 基于位置的社会化网络推荐系统[J]. 计算机学报,2015, 38(2):322-336. Liu S D, Meng X W. Recommender systems in location-based social networks[J]. Chinese Journal of Computers, 2015, 38(2):322-336. (in Chinese)
[7] Yuan Q, Cong G, Ma Z. Time-aware point-of-interest recommendation[C]//Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2013). Dublin, Ireland, 2013:363-372.
[8] 李普聪,钟元生. 移动O2O商务线下商家采纳行为研究[J]. 当代财经,2014(9):75-87. Li P C, Zhong Y S. A study of ofine merchants' acceptance of mobile O2O commerce[J]. Contemporary Finance & Economics, 2014(9):75-87. (in Chinese).
[9] Adomavicius G, Tuzhilin A. Towards the next generation of recommender systems:a survey of the state-of-the-art and possible extensions[J]. IEEE Transactions on Knowledge and Data Engineering, 2005,17(6):734-749.
[10] 许海玲,吴潇,李晓东,阎保平. 互联网推荐系统比较研究[J]. 软件学报,2009, 20(2):350-362. Xu H L, Wu X, Li X D, Yan B P. Comparison study of Internet recommendation systems[J]. Journal of Software, 2009, 20(2):350-362. (in Chinese)
[11] Liu J X, Tang M D, Zheng Z B, Liu X Q, Lü S X. Location-aware and personalized collaborative fltering for Web service recommendation[J]. IEEE Transactions on Services Computing, 2015, 99:1-14.
[12] 徐雅斌,孙晓晨. 位置社交网络的个性化位置推荐[J]. 北京邮电大学学报,2015, 38(5):118-124. Xu Y B, Sun X C. Individual location recommendation for location-based social network[J]. Journal of Beijing University of Posts and Telecommunications, 2015, 38(5):118-124. (in Chinese)
[13] Liu S D, Meng X W. A location-based business information recommendation algorithm[J]. Mathematical Problems in Engineering, 2015(4):1-9.
[14] Li W M, Yao M k, Zhou X K, Nishimura S, Jin Q. Recommendation of location-based services based on composite measures of trust degree[J]. Journal of Supercomputing, 2014, 69(3):1154-1165.
[15] 王海艳,杨文彬,王随昌,李思瑞. 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 H Y, Yang W B, Wang S C, Li S R. A service recommendation method based on trustworthy community[J]. Chinese Journal of Computers, 2014, 37(2):301-311. (in Chinese)
[16] Deng S G, Huang L T, Xu G D. Social network-based service recommendation with trust enhancement[J]. Expert Systems with Applications, 2014, 41(18):8075-8084
[17] 张佩云,陈恩红,黄波. 基于社会网络面向个性化需求的可信服务推荐[J]. 通信学报,2013, 34(12):49-59. Zhang P Y, Chen E H, Huang B. Personalized requirements oriented trustworthy services recommendation based on social network[J]. Journal on Communications, 2013, 34(12):49-59. (in Chinese)
[18] 刘树栋,孟祥武. 一种基于移动用户位置的网络服务推荐方法[J]. 软件学报,2014, 25(11):2556-2574. Liu S D, Meng X W. Approach to network services recommendation based on mobile users' location[J]. Journal of Software, 2014, 25(11):2556-2574. (in Chinese)
[19] Sattari M, Toroslu I H, Karagoz P, Symeonidis P, Manolopoulos Y. Extended feature combination model for recommendations in location-based mobile services[J]. Knowledge & Information Systems, 2014, 44(3):629-661
[20] 张志军,刘弘. 上下文感知的移动社交网络推荐算法研究[J]. 模式识别与人工智能,2015, 28(5):404-410. Zhang Z J, Liu H. Research on context-awareness mobile SNS recommendation algorithm[J]. Pattern Recognition and Artifcial Intelligence, 2015, 28(5):404-410. (in Chinese)
[21] Xiao S, Dong M. Hidden semi-Markov model-based reputation management system for online to ofine (O2O) e-commerce markets[J]. Decision Support Systems, 2015(77):87-99.
[22] Min L, Xu Y, Ou H Y. The measurement of the consumer trust to O2O e-commerce based on fuzzy evaluation[C]//2014 Seventh International Joint Conference on Computational Sciences and Optimization, Beijing, China, 2014:113-116.
[23] Yang Z J. An organizational mode with reputation for O2O e-commerce[M]. Berlin, Heidelberg:Springer, 2014.
[24] 孟祥武,刘树栋,张玉洁,胡勋. 社会化推荐系统研究[J]. 软件学报,2015, 26(6):1356-1372. Meng X W, Liu S D, Zhang Y J, Hu X. Research on social recommender systems[J]. Journal of Software, 2015, 26(6):1356-1372. (in Chinese)
[25] Cantador I, Brusilovsky P, Kuflik T. Second workshop on information heterogeneity and fusion in recommender systems[C]//Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec2011), 2011:387-388.
[26] 朱郁筱,吕琳媛. 推荐系统评价指标综述[J]. 电子科技大学学报,2012, 41(2):164-175. Zhu Y X, Liu L Y. Evaluation metrics for recommender systems[J]. Journal of University of Electronic Science and Technology of China, 2012, 41(2):164-175. (in Chinese)
[27] Su X, Khoshgoftaar T M. A survey of collaborative fltering techniques[J]. Advances in Artifcial Intelligence, 2009, 2009(12):189-201.
[28] Huang C L, Huang W L. Handling sequential pattern decay:developing a two stage collaborative recommender system[J]. Electronic Commerce Research & Applications, 2009, 8(3):117-129.
[29] Rafeh R, Bahrehmand A. An adaptive approach to dealing with unstable behaviour of users in collaborative fltering systems[J]. Journal of Information Science, 2012, 38:205-221.

基于声誉计算的可信O2O服务提供商推荐方法

Trustworthiness Recommendation for O2O Service Providers Based on Their Reputation

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

本文评价

[1]	马鑫, 吴云, 鹿泽光. 基于混合神经网络的协同过滤推荐模型[J]. 应用科学学报, 2020, 38(3): 478-487.
[2]	程树林, 张博锋, 邹国兵. 基于用户和项目的双视角协同过滤推荐方法[J]. 应用科学学报, 2017, 35(3): 326-336.
[3]	黄东晋，唐鹏斌，王音，丁友东. 基于交互历史的三维家居个性化推荐与虚拟展示[J]. 应用科学学报, 2015, 33(4): 407-418.