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New Technology of Library and Information Service  2015, Vol. 31 Issue (3): 49-57    DOI: 10.11925/infotech.1003-3513.2015.03.07
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Information Resource Recommendation Method Based on Dynamic Tag-Resource Network
Wang Zhongqun, Jiang Sheng, Xiu Yu, Huang Subin, Wang Qiansong
School of Computer and Information, Anhui Polytechnic University, Wuhu 241000, China
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[Objective] To solve the problem that recommender systems recommend outdated information resources to the target user. [Methods] This paper proposes an individual recommendation method for information resource based on dynamic tag resources network graph. Firstly, resource network graph is established to form resource semantic relationships, using common tags in two resource objects as a link pairwise. Secondly, tag network graph with time is created to describe users' interest drifting using the links in resource network graph. Thirdly, top N information resource objects are recommended to target user from tag network graph by matching target users' dynamic tags describing users' interest drifting. [Results] In MovieLens data set, the experimental results show that this information recommendation method can trace and predict users' interest drifting, and recommend accurate resource to users. Mean Absolute Error (MAE) is lower than the traditional methods by about 15%. [Limitations] The method does not involve the problem that information resources are recommended under real-time dynamic environment such as information retrieval with users' interests drifting rapidly. [Conclusions] The proposed method can recommend more accurate information resource to users with interest drifting.

Key wordsSocial tags      Resource network graph      Tag network graph      Interest drifting      Resource recommendation     
Received: 04 September 2014      Published: 16 April 2015
:  TP393  

Cite this article:

Wang Zhongqun, Jiang Sheng, Xiu Yu, Huang Subin, Wang Qiansong. Information Resource Recommendation Method Based on Dynamic Tag-Resource Network. New Technology of Library and Information Service, 2015, 31(3): 49-57.

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[1] 于洪, 李转运. 基于遗忘曲线的协同过滤推荐算法[J]. 南京大学学报: 自然科学版, 2010, 46(5): 520-527. (Yu Hong, Li Zhuanyun. A Collaborative Filtering Recommendation Algorithm Based on Forgetting Curve [J]. Journal of Nanjing University: Natural Sciences, 2010, 46(5): 520-527.)
[2] 冯勇, 李军平, 徐红艳, 等. 基于社会网络分析的协同推荐方法改进[J]. 计算机应用, 2013, 33(3): 841-844. (Feng Yong, Li Junping, Xu Hongyan, et al. Collaborative Recommendation Method Improvement Based on Social Network Analysis [J]. Journal of Computer Applications, 2013, 33(3): 841-844.)
[3] 叶红云, 倪志伟, 倪丽萍. 一种检测兴趣漂移的图结构推荐系统[J]. 小型微型计算机系统, 2012, 33(4): 700-706. (Ye Hongyun, Ni Zhiwei, Ni Liping. Novel Graph-based Recommender System with Interest Drift Detection [J]. Journal of Chinese Computer Systems, 2012, 33(4): 700-706.)
[4] 王超, 吕俊生. 国内外学术信息推荐方法研究进展[J]. 情报杂志, 2013, 32(9): 142-147. (Wang Chao, Lv Junsheng. Progress in the Research on Academic Information Recommendation Mothods [J]. Journal of Information, 2013, 32(9): 142-147.)
[5] 王大玲, 冯时, 张一飞, 等. 社会媒体多模态、多层次资源推荐技术研究[J]. 智能系统学报, 2014, 9(3): 265-275. (Wang Daling, Feng Shi, Zhang Yifei, et al. Study on the Recommendations of Multi-modal and Multi-level Resources in Social Media [J]. CAAI Transactions on Intelligent Systems, 2014, 9(3): 265-275.)
[6] 郭新明, 弋改珍. 混合模型的用户兴趣漂移算法[J]. 智能系统学报, 2010, 5(2): 181-184. (Guo Xinming, Yi Gaizhen. A Hybrid Algorithm to Track Drift of User's Interests [J]. CAAI Transactions on Intelligent Systems, 2010, 5(2): 181-184. )
[7] 俞琰, 邱广华. 用户兴趣变化感知的重启动随机游走推荐算法研究[J]. 现代图书情报技术, 2012(4): 48-53. (Yu Yan, Qiu Guanghua. Research on User Interest Shift Aware Random Walk with Restart Recommendation Algorithm [J]. New Technology of Library and Information Service, 2012(4): 48-53.)
[8] Min S, Han I. Detection of the Customer Time-Variant Pattern for Improving Recommender Systems [J]. Expert System with Application, 2005, 28(2): 189-199.
[9] Cao H, Chen E H, Yang J, et al. Enhancing Recommender Systems Under Volatile User Interest Drifts [C]. In: Proceedings of the 18th ACM Conference on Information and Knowledge Mangement. ACM, 2009: 1257-1266.
[10] Wu P, Yeung C H, Liu W, et al. Time-aware Collaborative Filtering with the Piecewise Decay Function [OL]. [2014-11-13].
[11] Cheng Y, Qiu G, Bu J, et al. Model Bloggers' Interests Based on Forgetting Mechanism [C]. In: Proceedings of the 17th International Conference on World Wide Web, Beijing, China. ACM, 2008:1129-1130.
[12] Klinkenberg R. Learning Drifting Concepts: Example Selection vs. Example Weighting [J]. Intelligent Data Analysis, 2004, 8(3): 281-300.
[13] McNally K, O'Mahony M P, Smyth B. A Comparative Study of Collaboration-based Reputation Models for Social Recommender Systems [J]. User Modeling and User-Adapted Interaction, 2014, 24(3): 219-260.
[14] Symeonidis P, Tiakas E, Manolopoulos Y. Product Recommendation and Rating Prediction Based on Multi-modal Social Networks [C]. In: Poceedings of the 5th ACM Conference on Recommender Systems. ACM Press, 2011: 61-68.
[15] 贾大文, 曾承, 彭智勇, 等. 一种基于用户偏好自动分类的社会媒体共享和推荐方法[J]. 计算机学报, 2012, 35(11): 2381-2391. (Jia Dawen, Zeng Cheng, Peng Zhiyong, et al. A User Preference Based Automatic Potential Group Generation Method for Social Media Sharing and Recommendation [J]. Chinese Journal of Computers, 2012, 35(11): 2381-2391.)
[16] 曾子明, 张振. 社会化标注系统中基于社区标签云的个性化推荐研究[J]. 情报杂志, 2011, 30 (10): 128-133. (Zeng Ziming, Zhang Zhen. A Personalized Recommendation Approach Based on Community Tag Cloud in Social Tagging System [J]. Journal of Intelligence, 2011, 30(10): 128-133.)
[17] 何继媛, 窦永香, 刘东苏. 大众标注系统中基于本体的语义检索研究综述[J]. 现代图书情报技术, 2011(3): 51-56. (He Jiyuan, Dou Yongxiang, Liu Dongsu. Survey of Ontology- based Semantic Retrieval in Folksonomy [J]. New Technology of Library and Information Service, 2011(3): 51-56.)
[18] 易明, 曹高辉, 毛进, 等. 基于Tag的知识主题网络构建与Web知识推送研究[J]. 中国图书馆学报, 2011, 37(4): 4-12. (Yi Ming, Cao Gaohui, Mao Jin, et al. Knowledge Topic Network Construction and Web Knowledge Push Based on Tag [J]. Journal of Library Science in China, 2011, 37(4): 4-12.)
[19] 周朴雄, 陈涛. 虚拟社区中基于相似标签聚类的语义信息推荐[J]. 情报理论与实践, 2013, 36(10): 100-104. (Zhou Puxiong, Chen Tao. Semantic Information Recommendation Based on Similar Tags Clustering in Virtual Community [J]. Information Studies: Theory & Application, 2013, 36(10): 100-104.)
[20] 韩敏, 唐常杰, 段磊, 等. 基于TF-IDF相似度的标签聚类方法[J]. 计算机科学与探索, 2010, 4(3): 240-246. (Han Min, Tang Changjie, Duan Lei, et al. TF-IDF Similarity Based Method for Tag Clustering [J]. Journal of Frontiers of Computer Science and Technology, 2010, 4(3): 240-246.)

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