To help e-Commerce websites provide personalized recommendation management based on collaborative filtering, an e-Commerce collaborative filtering prototype that is called ECRec, is proposed and implemented. ECRec includes two basic algorithms and four improved algorithms, and its architecture is independent on e-Commerce business systems,consequently, ECRec has a better portability and maintainability. Moreover, the algorithm interface in ECRec is embedded, thus ECRec has the characteristics of open architecture, and websites can add more collaborative filtering algorithms into ECRec.
李聪. ECRec: 基于协同过滤的电子商务个性化推荐管理*[J]. 现代图书情报技术, 2009, (10): 34-39.
Li Cong. ECRec:e-Commerce Personalized Recommendation Management Based on Collaborative Filtering. New Technology of Library and Information Service, 2009, (10): 34-39.
[1] Borchers A, Herlocker J, Konstan J A, et al. Ganging up on Information Overload[J]. Computer, 1998, 31(4): 106-108.
[2] Schafer J B, Konstan J A, Riedl J. Recommender Systems in e-Commerce[C]. In: Proceedings of the 1st ACM Conference on Electronic Commerce. New York: ACM Press, 1999:158-166.
[3] Schafer J B, Konstan J A, Riedl J. e-Commerce Recommendation Applications[J]. Data Mining and Knowledge Discovery, 2001, 5(1-2): 115-153.
[4] Demiriz A. Enhancing Product Recommender Systems on Sparse Binary Data[J]. Data Mining and Knowledge Discovery, 2004, 9(2): 147-170.
[5] Karypis G. Evaluation of Item-based Top-n Recommendation Algorithms[C]. In: Proceedings of the 10th International Conference on Information and Knowledge Management. New York: ACM Press, 2001: 247-254.
[6] Sarwar B M. Sparsity, Scalability, and Distribution in Recommender Systems[D]. Minneapolis, MN: University of Minnesota, 2001.
[7] Deshpande M, Karypis G. Item-based Top-n Recommendation Algorithms[J]. ACM Transactions on Information Systems, 2004, 22(1): 143-177.
[8] Shardanand U, Maes P. Social Information Filtering: Algorithms for Automating “ Word of Mouth”[C]. In: Proceedings of the 1995 ACM SIGCHI Conference on Human Factors in Computing Systems. New York: ACM Press, 1995:210-217.
[9] Rosenthal R, Rosnow R. Essentials of Behavioral Research: Methods and Data and Analysis[M]. 2nd Edition. New York: McGraw-Hill, 1991.
[10] Balabanovié M, Shoham Y. Fab: Content-based, Collaborative Recommendation[J]. Communications of the ACM, 1997, 40(3): 66-72.
[11] Nichols D M. Implicit Rating and Filtering[C]. In: Proceedings of the 5th DELOS Workshop on Filtering and Collaborative Filtering. Sophia Antipolis, France: ERCIM, 1997:31-36.
[12] Resnick P, Iacovou N, Suchak M, et al. Grouplens: An Open Architecture for Collaborative Filtering of Netnews[C]. In: Proceediings of the 1994 ACM on Computer Supported Cooperative Work. New York: ACM Press, 1994:175-186.
[13] Sarwar B, Karypis G, Konstan J, et al. Item-based Collaborative Filtering Recommendation Algorithms[C]. In: Proceediings of the 10th International Conference on World Wide Web. New York: ACM Press, 2001:285-295.
[14] 李聪, 梁昌勇, 马丽. 基于领域最近邻的协同过滤推荐算法[J]. 计算机研究与发展, 2008, 45(9): 1532-1538.
[15] 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,待发.
[16] 李聪. 电子商务推荐系统中协同过滤瓶颈问题研究[D]. 合肥: 合肥工业大学, 2009.
[17] 李聪, 梁昌勇. 适应用户兴趣变化的协同过滤增量更新机制[J]. 情报学报,待发.
[18] 霍华, 冯博琴. 基于压缩稀疏矩阵矢量相乘的文本相似度计算[J]. 小型微型计算机系统, 2005, 26(6): 988-990.
[19] 严蔚敏, 吴伟民. 数据结构(C语言版)[M]. 北京: 清华大学出版社, 2002.
[20] 李聪, 梁昌勇. 基于属性值偏好矩阵的协同过滤推荐算法[J]. 情报学报, 2008, 27(6): 884-890.