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New Technology of Library and Information Service  2014, Vol. 30 Issue (5): 50-57    DOI: 10.11925/infotech.1003-3513.2014.05.07
Model for Personalized Recommendation Based on Social Tagging in P2P Environment
Zhao Yan, Wang Yamin
School of Economics & Management, Xidian University, Xi’an 710126, China
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[Objective] Utilizing tags frequency and time used by the user, discussing the impact of dynamic changes of user interest for personalized recmmendation accuracy. [Methods] Constructing model for personalized recom-mendation based on social tagging in P2P environment, illustrating the calculation of user preferences and recommended process in detail. Making an experiment to verify the validity of the model using P2P movie sharing system. [Results] In 10 randomly selected target users, the hit rate of recommendation for eight users is higher than traditonal collabrative filtering which is based on scores, proving the advantages of making full use of tag frequency and time factor to recommend. [Limitations] Due to the main task of this paper is to reseach the impact of dynamic changes of user interst for personalized recommendation, so only delete meaningless tags and merge similar tags by hands, do not have an effective mechanism to control the ambiguity of tags. [Conclusions] Considering the dynamic changes of user interest can help to improve the accuracy of personalized recommendation.

Key wordsSocial tagging      Personalized recommendation      Tag preference vector      P2P     
Received: 02 December 2013      Published: 06 June 2014
:  G354  

Cite this article:

Zhao Yan, Wang Yamin. Model for Personalized Recommendation Based on Social Tagging in P2P Environment. New Technology of Library and Information Service, 2014, 30(5): 50-57.

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[1] 韩定一. 对等网络的社区模型及其在搜索中的应用[D].上海: 上海交通大学, 2007. (Han Dingyi. Community Models in Peer-to-Peer Networks and Their Applications in Search [D]. Shanghai: Shanghai Jiaotong University, 2007.)
[2] 魏建良, 朱庆华. 社会化标注理论研究综述[J]. 中国图书馆学报, 2009, 35(6): 88-96. (Wei Jianliang, Zhu Qinghua. A Review of the Study of Social Tagging Theory [J]. Journal of Library Science in China, 2009, 35(6): 88-96.)
[3] 陈洁, 司莉. 社会分类法(Folksonomy)特点及其应用研究[J]. 图书与情报, 2008(3): 27-30. (Chen Jie, Si Li. Research on Characteristics and Applications of Folksonomy[J]. Library&Information, 2008(3): 27-30.)
[4] 徐志玮, 郑建瑜. 社会化标签特性及研究进展综述[J]. 图书馆建设, 2013(5): 88-91. (Xu Zhiwei, Zheng Jianyu. Review on the Characteristic of Social Tags and Its Research Progress [J]. Library Development, 2013(5): 88-91.)
[5] Görlitz O, Sizov S, Staab S.PINTS: Peer-to-peer Infras-tructure for Tagging System[C]. In: Proceedings of the 7th International Conference on Peer-to-Peer Systems (IPTPS). Berkeley, CA, USA: USENIX Association, 2008.
[6] Fokker J, Pouwelse J, Buntine W. Tag-Based Navigation for Peer-to-Peer Wikipedia[OL].[2013-10-20].http://bioinfor-matics.
[7] Dattolo A, Ferrara F, Tasso C. Neighbor Selection and Recommendations in Social Bookmarking Tools [C]. In: Proceedings of the 9th International Conference on Intelligent Systems Design and Applications. 2009: 267-272.
[8] Chen H, Dumais S.Bringing Order to the Web: Automatically Categorizing Search Results[C]. In:Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM, 2000: 145-152.
[9] Shiratsuchi K, Yoshii S, Furukawa M. Finding Unknown Interests Utilizing the Wisdom of Crowds in a Social Bookmark Service[C]. In: Proceedings of the 2006 IEEE/ WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. Washington DC: IEEE Computer Society, 2006: 421-424.
[10] 易明, 邓卫华, 徐佳. 社会化标签系统中基于组合策略的个性化知识推荐研究[J].情报科学, 2011, 29(7): 1093-1097. (Yi Ming, Deng Weihua, Xu Jia. Study on Personalized Knowledge Recommendation Based on Hybrid Strategy in Tag System [J]. Information Science, 2011, 29(7): 1093-1097.)
[11] 刘健, 尹春霞, 原福永. 基于非结构化P2P网络用户模型的协同过滤推荐机制[J]. 山东大学学报: 理学版, 2011, 46(5): 28-33. (Liu Jian, Yin Chunxia, Yuan Fuyong. A Collaborative Filtering Recommendation Mechanism Based on User Profile in Unstructured P2P Networks [J]. Journal of Shandong University: Natural Science, 2011, 46(5): 28-33.)
[12] 魏建良, 朱庆华. 基于社会化标注的个性化推荐研究进展[J]. 情报学报, 2010, 29(4): 625-633. (Wei Jianliang, Zhu Qinghua. Advances in Personalized Information Recommen-dation Based on Social Tagging [J]. Journal of the China Society for Scientific and Technical Information, 2010, 29(4): 625-633.)
[13] 鲁欣, 周伟锋. 基于认知心理互动的网络信息组织的思考[J]. 图书馆学研究, 2008(5): 30-33. (Lu Xin, Zhou Weifeng. Reflect on Organization of Network Information Based on Interaction of Cognitive Psychology [J]. Researches in Library Science, 2008(5): 30-33.)
[14] Van Setten M, Brussee R, Van Vliet H, et al.On the Importance of ""Who Tagged What"" [C].In:Proceedings of the Workshop on the Social Navigation and Community Based Adaptation Technologies at AH 2006, Dublin, Ireland. 2006.
[15] Au Yeung C , Gibbins N, Shadbolt N. A Study of User Profile Generation from Folksonomies [C]. In: Proceedings of the Workshop on Social Web and Knowledge Management at the 17th International Conference on World Wide Web,Beijing, China.2008.
[16] 郭伟光, 李道芳, 章蕾. 一种社会化标注系统资源个性化推荐方法[J]. 计算机工程与应用, 2011, 47(10): 240-243. (Guo Weiguang, Li Daofang, Zhang Lei. Personalized Resource Recommendation Method in Social Tagging System [J]. Computer Engineering and Applications, 2011, 47(10): 240-243.)
[17] Cheng Y, Qiu G, Bu J J, et al. Model Bloggers'Interests Based on Forgetting Mechanism[C]. In: Proceedings of the 17th International Conference on World Wide Web. New York: ACM Press, 2008:1129-1130.
[18] 张小红. 协同过滤中的相似性度量方法的研究[J]. 无线电通信技术, 2013, 39(1): 94-96. (Zhang Xiaohong. Research on Similarity Metrics for Collaborative Filtering [J]. Radio Communications Technology, 2013, 39(1): 94-96.)

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