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New Technology of Library and Information Service  2015, Vol. 31 Issue (12): 28-33    DOI: 10.11925/infotech.1003-3513.2015.12.05
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Research on Multi-granularity Users' Preference Mining Based on Collaborative Filtering Personalized Recommendation
Song Meiqing
School of Information Management, Wuhan University, Wuhan 430072, China
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[Objective] Researching the relationship between users' preference mining granularity and mining efficiency in collaborative filtering, this paper aims at finding out the most efficient mining granularity. [Methods] According to the practical application, the users' preference mining granularity is divided into three kinds from coarse-grained to fine-grained, and then design the corresponding preference mining algorithm under the three kinds of granularities, finally contrast users' preference mining efficiency under different granularities through experiments. [Results] Experimental results show that the preference mining efficiency reduces as the users' preference mining granularity changes from coarse to fine. [Limitations] Data only includes users' consumption data and rating data, other types of data are not covered temporarily. [Conclusions] Coarse-grained preference mining is better for discovering users' preferences.

Received: 05 June 2015      Published: 06 April 2016
:  G202  

Cite this article:

Song Meiqing. Research on Multi-granularity Users' Preference Mining Based on Collaborative Filtering Personalized Recommendation. New Technology of Library and Information Service, 2015, 31(12): 28-33.

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[1] Liu H, Hu Z, Mian A, et al. A New User Similarity Model to Improve the Accuracy of Collaborative Filtering [J]. Knowledge-based Systems, 2014, 56: 156-166.
[2] Bobadilla J, Serradilla F, Bernal J. A New Collaborative Filtering Metric that Improves the Behavior of Recommender Systems [J]. Knowledge-based Systems, 2010, 23(6): 520-528.
[3] 王海艳, 张大印. 一种可信的基于协同过滤的服务选择模型[J]. 电子与信息学报, 2013, 35(2): 349-354. (Wang Haiyan, Zhang Dayin. A Trustworthy Service Selection Model Based on Collaborative Filtering [J]. Journal of Electronics & Information Technology, 2013, 35(2): 349-354.)
[4] 刘胜宗, 廖志芳, 吴言凤, 等. 一种融合用户评分可信度和相似度的协同过滤算法[J]. 小型微型计算机系统, 2015, 35(5): 973-977. (Liu Shengzong, Liao Zhifang, Wu Yanfeng, et al. A Collaborative Filtering Algorithm Combined with User Rating Credibility and Similarity [J]. Journal of Chinese Computer Systems, 2015, 35(5): 973-977.)
[5] 孙光福, 吴乐, 刘淇, 等. 基于时序行为的协同过滤推荐算法[J]. 软件学报, 2013, 24(11): 2721-2733. (Sun Guangfu, Wu Le, Liu Qi, et al. Recommendations Based on Collaborative Filtering by Exploiting Sequential Behaviors [J]. Journal of Software, 2013, 24(11): 2721-2733.)
[6] 郑志高, 刘京, 王平, 等. 时间加权不确定近邻协同过滤算法[J]. 计算机科学, 2014, 41(8): 7-12. (Zheng Zhigao, Liu Jing, Wang Ping, et al. Time-weighted Uncertain Nearest Neighbor Collaborative Filtering Algorithm [J]. Computer Science, 2014, 41(8): 7-12.)
[7] Nilashi M, Jannach D, Ibrahim O, et al. Clustering-and Regression-based Multi-criteria Collaborative Filtering with Incremental Updates [J]. Information Sciences, 2015, 293: 235-250.
[8] 张莉, 秦桃, 滕丕强. 一种改进的基于用户聚类的协同过滤算法[J]. 情报科学, 2014, 32(10): 24-27, 32. (Zhang Li, Qin Tao, Teng Piqiang. An Improved Collaborative Filtering Algorithm Based on User Clustering [J]. Information Science, 2014, 32(10): 24-27, 32.)
[9] 邓晓懿, 金淳, 韩庆平, 等. 基于情境聚类和用户评级的协同过滤推荐模型[J]. 系统工程理论与实践, 2013, 33(11): 2945-2953. (Deng Xiaoyi, Jin Chun, Han Jim C, et al. Improved Collaborative Filtering Model Based on Context Clustering and User Ranking [J]. Systems Engineering- Theory & Practice, 2013, 33(11): 2945-2953.)
[10] 于洪, 李俊华. 结合社交与标签信息的协同过滤推荐算法[J]. 小型微型计算机系统, 2013, 34(11): 2467-2471. (Yu Hong, Li Junhua. Collaborative Filtering Recommendation Algorithm Using Social and Tag Information [J]. Journal of Chinese Computer Systems, 2013, 34(11): 2467-2471.)
[11] 俞琰, 邱广华. 融合社会网络的协同过滤推荐算法研究[J]. 现代图书情报技术, 2012(6): 54-59. (Yu Yan, Qiu Guanghua. Research on Collaborative Filtering Recommendation Algorithm by Fusing Social Network [J]. New Technology of Library and Information Service, 2012(6): 54-59.)
[12] 李聪, 梁昌勇. 基于n序访问解析逻辑的协同过滤冷启动消除方法[J]. 系统工程理论与实践, 2012, 32(7): 1537-1545. (Li Cong, Liang Changyong. Cold-start Eliminating Method of Collaborative Filtering Based on N-sequence Access Analytic Logic [J]. Systems Engineering- Theory & Practice, 2012, 32(7): 1537-1545.)
[13] 杨兴耀, 于炯, 吐尔根·依布拉音, 等.融合奇异性和扩散过程的协同过滤模型[J]. 软件学报, 2013, 24(8): 1868-1884. (Yang Xingyao, Yu Jiong, Turgun Ibrahimi, et al. Collaborative Filtering Model Fusing Singularity and Diffusion Process [J]. Journal of Software, 2013, 24(8): 1868-1884.)
[14] Sarwar B, Karypis G, Konstan J, et al. Item-based Collaborative Filtering Recommendation Algorithms [C]. In: Proceedings of the 10th International Conference on World Wide Web. 2001: 285-295.
[15] 项亮. 推荐系统实现[M]. 北京: 人民邮电出版社, 2012. (Xiang Liang. Recommendation System Practice [M]. Beijing: Posts & Telecom Press, 2012.)
[16] Pazzani M, Billsus D. Learning and Revising User Profiles: The Identification of Interesting Web Sites [J]. Machine Learning, 1997, 27: 313-331

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