[Objective] In order to improve the quality of recommendation, rough set is introduced into collaborative filtering based on user clustering. [Methods] This paper proposes a collaborative filtering recommendation model based on rough user clustering. When off-line, it clusters all users by rough K-means user clustering algorithm, which assigns user to upper or lower approximation based on similarity and thus generates his initial neighbor. When on-line, the model starts searching the nearest neighbor from the target user's initial neighbor, forecasts his ratings and makes recommendation. [Results] Experimental results show that the proposed model decreases the Mean Absolute Error (MAE) about 14% when compared with traditional and item-based collaborative filtering, and decreases MAE about 10% when compared with collaborative filtering based on user clustering. [Limitations] When considering the importance of upper and lower approximation to adjusting the centroid of cluster, this paper ignores the impact of the number of user clusters and the threshold of the number of nearest neighbors. [Conclusions] This model can effectively improve recommendation accuracy, and has high feasibility and practical significance.
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, 31(1): 45-51.
Wang Xiaoyun, Qian Lu, Huang Shiyou. Collaborative Filtering Recommendation Model Based on Rough User Clustering. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2015.01.07.
[1] Lu L Y, Medo M, Yeung C H, et al. Recommender Systems [J]. Physics Reports-Review Section of Physics Letters, 2012, 519(1): 1-49.
[2] Breese J S, Hecherman D, Kadie C. Empirical Analysis of Predictive Algorithm for Collaborative Filtering [C]. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, Madison, USA. San Francisco: Morgan Kaufmann Publishers, 1998: 43-52.
[3] Park D H, Kim H K, Choi I Y, et al. A Literature Review and Classification of Recommender Systems Research [J]. Expert Systems with Applications, 2012, 39(11): 10059-10072.
[4] Herlocker J L, Konstan J A, Borchers A, et al. An Algorithmic Framework for Performing Collaborative Filtering [C]. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, USA. ACM, 1999: 230-237.
[5] 梁昌勇, 李聪, 杨善林. 一种基于Rough集理论的最近邻协同过滤算法[J]. 情报学报, 2009, 28(5): 712-719. (Liang Changyong, Li Cong, Yang Shanlin. A Nearest-Neighbor Collaborative Filtering Algorithm Based on Rough Set Theory [J]. Journal of the China Society for Scientific and Technical Information, 2009, 28(5): 712-719.)
[6] Takacs G, Pilaszy I, Nemeth B, et al. Scalable Collaborative Filtering Approaches for Large Recommender System [J]. Journal of Machine Learning Research, 2009,10: 623-656.
[7] Kim H N, Ji A T, Ha I, et al. Collaborative Filtering Based on Collaborative Tagging for Enhancing the Quality of Recommendation [J]. Electronic Commerce Research and Applications, 2010, 9(1): 73-83.
[8] Braak P T, Abdullah N, Xu Y. Improving the Performance of Collaborative Filtering Recommender Systems through User Profile Clustering [C]. In: Proceedings of IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, Milan, Italy. IEEE, 2009: 147-150.
[9] Ungar L H, Foster D P, Andre E, et al. Clustering Methods for Collaborative Filtering [C]. In: Proceedings of 1998 Workshop on Recommender Systems. AAAI Press, 1998: 114-129.
[10] 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.)
[11] 李涛, 王建东. 基于多层相似性用户聚类的推荐算法[J]. 南京航空航天大学学报, 2006, 38(6): 717-721. (Li Tao, Wang Jiandong. Clustering Basal Users Based Recommendation Algorithm Using Multiple-Level Similarity [J]. Journal of Nanjing University of Aeronautics& Astronautics, 2006, 38(6): 717-721.)
[12] Gong S, Huang C. Employing Fuzzy Clustering to Alleviate the Sparsity Issue in Collaborative Filtering Recommendation Algorithms [C]. In: Proceedings of 2008 International Pre-Olympic Congress on Computer Science. Liverpool, UK: World Academic Press, 2008: 449-454.
[13] Dcshpandc M, Karypis G. Item-based Top-N Recommendation Algorithms [J]. ACM Transactions on Information Systems, 2004, 22(1): 143-177.
[14] 周涛. 具有自适应参数的粗糙K-means聚类算法 [J]. 计算机工程与应用, 2010, 46(26): 7-10. (Zhou Tao. Adaptive Rough K-means Clustering Algorithm [J]. Computer Engineering and Applications, 2010, 46(26): 7-10.)
[15] Ruspini E H. A New Approach to Clustering [J]. Information and Control, 1969, 15(1): 22-32.
[16] Pawlak Z. Rough Sets [J]. International Journal of Computer and Information Sciences, 1982, 11(5): 341-356.
[17] Lingras P, West J. Interval Set Clustering of Web Users with Rough K-means [J]. Journal of Intelligent Information Systems, 2004, 23(1): 5-16.
[18] 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.
[19] Birtolo C, Ronca D. Advances in Clustering Collaborative Filtering by Means of Fuzzy C-means and Trust [J]. Expert Systems with Applications, 2013, 40(17): 6997-7009.
[20] 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.)
[21] 王明佳, 韩景倜, 韩松乔. 基于模糊聚类的协同过滤推荐算法 [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.)
[22] Saha A, Das D, Karmakar D, et al. Clustering Customer Transactions: A Rough Set Based Approach [C]. In: Proceedings of the ISCA 22nd International Conference Computers and Their Applications in Industry and Engineering, San Francisco, USA. Cary, North Carolina, USA: ISCA, 2009: 213-218.
[23] Tseng V S, Su J H, Wang B W, et al. A Novel Recommendation Method Based on Rough Set and Integrated Feature Mining [C]. In: Proceedings of the 3rd International Conference on Innovative Computing Information and Control, Dalian, China. IEEE, 2008. DOI: 10.1109/ICICIC. 2008.612.
[24] Chen D E, Ying Y L, Gong S J. A Collaborative Filtering Algorithm Based on Rough Set and Fuzzy Clustering [C]. In: Proceedings of the 5th International Conference on Fuzzy System and Knowledge Discovery, Shandong, China. IEEE, 2008: 17-20.
[25] 杜金涛. 基于粗糙集的协同推荐模型研究[D]. 杭州: 杭州电子科技大学, 2009. (Du Jintao. A Study on Collaborative Recommendation Model Based on Rough Set [D]. Hangzhou: Hangzhou Dianzi University, 2009.)
[26] 张腾飞, 成龙, 李云. 基于簇内不平衡度量的粗糙K-means聚类算法[J]. 控制与决策, 2013, 28(10):1479-1484. (Zhang Tengfei, Chen Long, Li Yun. Rough K-means Clustering Based on Unbalanced Degree of Cluster [J]. Control and Decision, 2013, 28(10): 1479-1484.)
[27] Ahn H J. A New Similarity Measure for Collaborative Filtering to Alleviate the New User Cold-Starting Problem [J]. Information Sciences, 2008, 178(1-2): 37-51.
[28] 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 (WWW'01). ACM, 2001: 285-295.
[29] GroupLens Research. MovieLens Movie Rating Data Set [EB/OL]. [2014-01-16]. http://movielens.umn.edu/login.
[30] Herlocker J L, Konstan J A, Terveen L G, et al. Evaluating Collaborative Filtering Recommender Systems [J]. ACM Transactions on Information Systems, 2004, 22(1): 5-53.
[31] Jeong B, Lee J, Cho H. Improving Memory-Based Collaborative Filtering via Similarity Updating and Prediction Modulation [J]. Information Sciences, 2010, 180(5): 602-612.
[32] Resnick P, Iakovou N, Sushak M, et al. GroupLens: An Open Architecture for Collaborative Filtering of Netnews [C]. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work (CSCW'94), Chapel Hill, North Carolina, USA. ACM, 1994: 175-186.
