[Objective] Solve the problems in the traditional collaborative filtering recommendation algorithm, such as sparse data and user's interests in different time being considered equally.[Methods] This paper proposes a collaborative filtering algorithm based on user's interest fuzzy clustering. In the algorithm, the model of user's interest consists of the stable interest and the current interest. Users are clustered by the fuzzy clustering according to the stable interest, then the nearest neighbours and the initial recommendation list can be obtained. The final recommendation list is generated by sorting the similarity between the each item of initial recommendation list and user current interest, on the basis of the initial recommendations. [Results] The Mean Absolute Error (MAE) of the proposed method is nearly 10% reduction verified on the MovieLens dataset, compared with the traditional method.[Limitations] All categories of projects are considered in the model of the user stable interest without special treatments, such as merge and delete.[Conclusions] The experiment result indicates that the recommendation accuracy of the advanced approach is more efficiency, compared with the traditional recommendation algorithm.
刘占兵, 肖诗斌. 基于用户兴趣模糊聚类的协同过滤算法[J]. 现代图书情报技术, 2015, 31(11): 12-17.
Liu Zhanbing, Xiao Shibin. Collaborative Filtering Recommended Algorithm Based on User's Interest Fuzzy Clustering. New Technology of Library and Information Service, 2015, 31(11): 12-17.
[1] 李涛, 王建东, 叶飞跃, 等. 一种基于用户聚类的协同过滤推荐算法[J]. 系统工程与电子技术, 2007, 29(7): 1178-1182. (Li Tao, Wang Jiandong, Ye Feiyue, et al. Collaborative Filtering Recommendation Algorithm Based on Clustering Basal Users [J]. Systems Engineering and Electronics, 2007, 29(7): 1178-1182.)
[2] 王荣, 李晋宏, 宋威. 基于关键字的用户聚类算法[J]. 计算机工程与设计, 2012, 33(9): 3553-3557, 3568. (Wang Rong, Li Jinhong, Song Wei. User Clustering Algorithm Based on Keywords [J]. Computer Engineering and Design, 2012, 33(9): 3553-3557, 3568.)
[3] 孙守义, 王蔚. 一种基于用户聚类的协同过滤个性化图书推荐系统[J]. 现代情报, 2008, 27(11): 139-142. (Sun Shouyi, Wang Wei. A Collaborative Filtering Personalized Book Recommendation System Based on User Clustering [J]. Modern Information, 2008, 27(11): 139-142.)
[4] Verma S K, Mittal N, Agarwal B. Hybrid Recommender System Based on Fuzzy Clustering and Collaborative Filtering [C]. In: Proceedings of the 4th International Conference on Computer and Communication Technology, Allahabad, India. IEEE, 2013: 116-120.
[5] 李华, 张宇, 孙俊华. 基于用户模糊聚类的协同过滤推荐研究[J]. 计算机科学, 2012, 39(12): 83-86. (Li Hua, Zhang Yu, Sun Junhua. Research on Collaborative Filtering Recommendation Based on User Fuzzy Clustering [J]. Computer Science, 2012, 39(12): 83-86.)
[6] 王晓耘, 钱璐, 黄时友. 基于粗糙用户聚类的协同过滤推荐模型[J]. 现代图书情报技术, 2015(1): 45-51. (Wang Xiaoyun, Qian Lu, Huang Shiyou. Collaborative Filtering Recommendation Model Based on Rough User Clustering [J]. New Technology of Library and Information Service, 2015(1): 45-51.)
[7] 王明佳, 韩景倜, 韩松乔. 基于模糊聚类的协同过滤算法[J]. 计算机工程, 2012, 38(24): 50-52. (Wang Mingjia, Han Jingti, Han Songqiao. Collaborative Filtering Algorithm Based on Fuzzy Clustering [J]. Computer Engineering, 2012, 38(24): 50-52.)
[8] 邢春晓, 高凤荣, 战思南, 等. 适应用户兴趣变化的协同过滤推荐算法[J]. 计算机研究与发展, 2007, 44(2): 296-301. (Xing Chunxiao, Gao Fengrong, Zhan Sinan, et al. A Collaborative Filtering Recommendation Algorithm Incorporated with User Interest Change [J]. Journal of Computer Research and Development, 2007, 44(2): 296-301.)
[9] Zhang Y C, Liu Y Z.A Collaborative Filtering Algorithm Based on Time Period Partition [C]. In: Proceedings of the 3rd International Symposium on Intelligent Information Technology and Security Informatics. 2010: 777-780.
[10] 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.)
[11] 刁祖龙, 张兴忠. 基于本体用户兴趣模型的个性化推荐系统[J]. 计算机应用与软件, 2013, 30(10): 155-158. (Diao Zulong, Zhang Xingzhong. Personalized Recommendation System Based on Ontology User Interest Model [J]. Computer Applications and Software, 2013, 30(10): 155-158.)
[12] Baltrunas L, Ricci F. Context-dependent Items Generation in Collaborative Filtering [C]. In: Proceedings of the 2009 Workshop on Context-aware Recommender Systems. New York: ACM, 2009.
[13] Berget I, Mevik B H, Nis T. New Modifications and Application of Fuzzy C-means Methodology [J]. Computational Statistics & Data Analysis, 2008, 52(5): 2403-2418.
[14] 张付志, 常俊风, 周全强. 基于模糊C均值聚类的环境感知推荐算法[J]. 计算机研究与发展, 2013, 50(10): 2185-2194. (Zhang Fuzhi, Chang Junfeng, Zhou Quanqiang. Context-Aware Recommendation Algorithm Based on Fuzzy C-means Clustering [J]. Journal of Computer Research and Development, 2013, 50(10): 2185-2194.)
[15] 宋艳娟, 陈振标. 个性化检索系统中用户兴趣模型的研究[J]. 计算机与数字工程, 2013, 41(2): 271-274. (Song Yanjuan, Chen Zhenbiao. User Interest Model in Personalized Retrieval System [J]. Computer & Digital Engineering, 2013, 41(2): 271-274.)
[16] 韩旭. 个性化推荐系统用户兴趣建模方式的研究[J]. 数字技术与应用, 2010(11): 44, 46. (Han Xu. User Interest Modeling in Personalized Recommendation System [J]. Digital Technology and Application, 2010(11): 44, 46.)
[17] 任保宁, 梁永全, 赵建立, 等. 基于多维度权重动态更新的用户兴趣模型[J]. 计算机工程, 2014, 40(9): 42-45. (Ren Baoning, Liang Yongquan, Zhao Jianli, et al. User Interest Model Based on Dynamic Update of Multi-dimensional Weight [J]. Computer Engineering, 2014, 40(9): 42-45.)
[18] 李克潮, 梁正友. 适应用户兴趣变化的指数遗忘协同过滤算法[J]. 计算机工程与应用, 2011, 47(13): 154-156. (Li Kechao, Liang Zhengyou. Exponential Forgetting Collaborative Filtering Recommendation Algorithm Incorporated with User Interest Change [J]. Computer Engineering and Applications, 2011, 47(13): 154-156.)
[19] 项亮, 陈义, 王益. 推荐系统实践[M]. 北京: 人民邮电出版社, 2012: 25-27. (Xiang Liang, Chen Yi, Wang Yi. Recommendation System Practice [M]. Beijing: People's Posts and Telecommunications Press, 2012: 25-27.)