Please wait a minute...
New Technology of Library and Information Service  2010, Vol. 26 Issue (11): 37-41    DOI: 10.11925/infotech.1003-3513.2010.11.06
article Current Issue | Archive | Adv Search |
Review of Scalability Problem in E-commerce Collaborative Filtering
Li Cong
School of Computer Science,Sichuan Normal University, Chengdu 610066, China
Download: PDF(483 KB)   HTML  
Export: BibTeX | EndNote (RIS)      
Abstract  

Based on the introduction of basic collaborative filtering algorithm, six kinds of techniques which are used to ameliorate the scalability problem are generalized, including clustering, probabilistic approach, dimensionality reduction, item-based, dataset reduction and linear model. The collaborative filtering algorithms with aforementioned techniques are commented emphatically, and their ideas are summarized in two points: reducing the neighborhood search space under the precondition of unaffected recommendation quality; periodically running user similarity measuring and neighborhood research offline to reduce the recommendation computation online. Two future research directions on the scalability problem in collaborative filtering are discussed finally, namely the collaborative filtering algorithm based on distributed structure, and the neighborhood search based on formal concept analysis.

Key wordsE-commerce      Recommender systems      Collaborative filtering      Scalability     
Received: 29 September 2010      Published: 04 January 2011
: 

C931

 

Cite this article:

Li Cong. Review of Scalability Problem in E-commerce Collaborative Filtering. New Technology of Library and Information Service, 2010, 26(11): 37-41.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2010.11.06     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2010/V26/I11/37


[1] Schafer J B, Konstan J A, Riedl J. E-commerce Recommendation Applications
[J]. Data Mining and Knowledge Discovery, 2001, 5(1-2): 115-153.

[2] 洪文兴, 翁洋, 朱顺痣,等. 垂直电子商务网站的混合型推荐系统
[J]. 系统工程理论与实践, 2010, 30(5): 928-935.

[3] 许海玲, 吴潇, 李晓东, 等. 互联网推荐系统比较研究
[J]. 软件学报, 2009, 20(2): 350-362.

[4] 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.

[5] 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.

[6] 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.

[7] 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.

[8] Rashid A M, Lam S K, Karypis G, et al. ClustKNN: A Highly Scalable Hybrid Model- & Memory-based CF Algorithm
[C]. In: Proceedings of the KDD Workshop on Web Mining and Web Usage Analysis. 2006.

[9] Linden G, Smith B, York J. Amazon.com Recommendations: Item-to-item Collaborative Filtering
[J]. IEEE Internet Computing, 2003, 7(1): 76-80.

[10] Sarwar B M, Karypis G, Konstan J A, et al. Application of Dimensionality Reduction in Recommender System—A Case Study
[C]. In: Proceedings of ACM Web KDD Workshop. Minneapolis: University of Minnesota, 2000.

[11] Sarwar B M, Karypis G, Konstan J, et al. Recommender Systems for Large-scale E-commerce: Scalable Neighborhood Formation Using Clustering
[C]. In: Proceedings of the 5th International Conference on Computer and Information Technology. 2002.

[12] Chee S H S, Han J, Wang K. RecTree: An Efficient Collaborative Filtering Method
[C]. In: Proceedings of the 3rd International Conference on Data Warehousing and Knowledge Discovery. London: Springer-Verlag, 2001:141-151.

[13] Kelleher J, Bridge D. RecTree Centroid: An Accurate, Scalable Collaborative Recommender
[C]. In: Proceedings of the 14th Irish Conference on Artificial Intelligence and Cognitive Science. 2003:89-94.

[14] Xue G, Lin C, Yang Q, et al. Scalable Collaborative Filtering Using Cluster-based Smoothing
[C]. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM Press, 2005:114-121.

[15] O’Conner M, Herlocker J. Clustering Items for Collaborative Filtering
[C]. In: Proceedings of the ACM SIGIR Workshop on Recommender Systems. 1999.

[16] 邓爱林, 左子叶, 朱扬勇. 基于项目聚类的协同过滤推荐算法
[J]. 小型微型计算机系统, 2004, 25(9): 1665-1670.

[17] Kim B M, Li Q. Probabilistic Model Estimation for Collaborative Filtering Based on Items Attributes
[C]. In: Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence. Washington, DC: IEEE Computer Society Press, 2004:185-191.

[18] Kohrs A, Merialdo B. Clustering for Collaborative Filtering Applications
[C]. In: Proceedings of the International Conference on Computational Intelligence for Modelling Control and Automation. Amsterdam, Netherlands: IOS Press, 1999:199-204.

[19] Castro P A D, Franca F O. Evaluating the Performance of a Biclustering Algorithm Applied to Collaborative Filtering—A Comparative Analysis
[C]. In: Proceedings of the 7th International Conference on Hybrid Intelligent Systems. Washington, DC: IEEE Computer Society Press, 2007:65-70.

[20] George T, Merugu S. A Scalable Collaborative Filtering Framework Based on Co-clustering
[C]. In: Proceedings of the 5th IEEE International Conference on Data Mining. Washington, DC: IEEE Computer Society Press, 2005:625-628.

[21] 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, 2010, 29(1): 59-66.

[22] 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.

[23] Hofmann T. Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis
[C]. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM Press, 2003:259-266.

[24] Hoffman T. Latent Semantic Models for Collaborative Filtering
[J]. ACM Transactions on Information Systems, 2004, 22(1): 89-115.

[25] 李超然, 徐雁斐, 张亮. 协同推荐PLSA模型的动态修正
[J]. 计算机工程, 2005, 31(20): 46-48.

[26] 张亮, 李敏强. 面向协同过滤的真实偏好高斯混合模型
[J]. 系统工程学报, 2007, 22(6): 613-619.

[27] Breese J S, Heckerman D, Kadie C. Empirical Analysis of Predictive Algorithms for Collaborative Filtering
[R]. Redmond: Microsoft Research, 1998.

[28] Pennock D M, Horvitz E, Lawrence S, et al. Collaborative Filtering by Personality Diagnosis: A Hybrid Memory-and Model-based Approach
[C]. In: Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence. San Francisco: Morgan Kaufmann Publishers, 2000: 473-480.

[29] Zeng C, Xing C, Zhou L. Similarity Measure and Instance Selection for Collaborative Filtering
[C]. In: Proceedings of the 12th International Conference on World Wide Web. New York: ACM Press, 2003:652-658.

[30] Rennie J D M, Srebro N. Fast Maximum Margin Matrix Factorization for Collaborative Prediction
[C]. In: Proceedings of the 22nd International Conference on Machine Learning. New York: ACM Press, 2005:713-719.

[31] Wu M. Collaborative Prediction via Ensembles of Matrix Factorizations
[C]. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2007:43-47.

[32] Chen G, Wang F, Zhang C. Collaboratice Filtering Using Orthogonal Nonnegative Matrix Tri-factorization
[J]. Information Processing and Management, 2009, 45(3): 368-379.

[33] Billsus D, Pazzani M J. Learning Collaborative Information Filters
[C]. In: Proceedings of the 15th International Conference on Machine Learning. San Francisco: Morgan Kaufmann Publishers, 1998:46-54.

[34] Sarwar B, Karypis G, Konstan J, et al. Incremental SVD-based Algorithms for Highly Scaleable Recommender Systems
[C]. In: Proceedings of the 5th International Conference on Computer and Information Technology. 2002.

[35] Goldberg K, Roeder T, Gupta D, et al. Eigentaste: A Constant Time Collaborative Filtering Algorithm
[J]. Information Retrieval, 2001, 4(2): 133-151.

[36] Kim D, Yum B J. Collaborative Filtering Based on Iterative Principal Component Analysis
[J]. Expert Systems with Applications, 2005, 28(4): 823-830.

[37] Honda K, Ichihashi H. Component-wise Robust Linear Fuzzy Clustering for Collaborative Filtering
[J]. International Journal of Approximate Reasoning, 2004, 37(2): 127-144.

[38] 王自强, 冯博琴. 个性化推荐系统中遗漏值处理方法的研究
[J]. 西安交通大学学报, 2004, 38(8): 808-810.

[39] 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. New York: ACM Press, 2001:285-295.

[40] Sarwar B M, Konstan J A, Borchers A, et al. Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System
[C]. In: Proceedings of the 1998 ACM Conference on Computer Supported Cooperative Work. New York: ACM Press, 1998:345-354.

[41] Yu K, Xu X, Ester M, et al. Feature Weighting and Instance Selection for Collaborative Filtering: An Information-theoretic Approach
[J]. Knowledge and Information systems, 2003, 5(2): 201-224.

[42] Lemire D, Maclachlan A. Slope One Predictors for Online Rating-based Collaborative Filtering
[C]. In: Proceedings of the 5th SIAM International Conference on Data Mining. 2005:471-476.

[43] Boucher-Ryan P D, Bridge D. Collaborative Recommending Using Formal Concept Analysis
[J].Knowledge-Based Systems, 2006, 19(5): 309-315.

[1] Xiaofeng Li,Jing Ma,Chi Li,Hengmin Zhu. Identifying Commodity Names Based on XGBoost Model[J]. 数据分析与知识发现, 2019, 3(7): 34-41.
[2] Chuanming Yu,Yajing Guo,Yutian Gong,Manyu Huang,Hufeng Peng. Evolution and Regional Differences of E-commerce Policies for Rural Poverty Reduction Based on Topic over Time Model[J]. 数据分析与知识发现, 2018, 2(7): 34-45.
[3] Jie Li,Fang Yang,Chenxi Xu. A Personalized Recommendation Algorithm with Temporal Dynamics and Sequential Patterns[J]. 数据分析与知识发现, 2018, 2(7): 72-80.
[4] Daoping Wang,Zhongyang Jiang,Boqing Zhang. Collaborative Filtering Algorithm Based on Gray Correlation Analysis and Time Factor[J]. 数据分析与知识发现, 2018, 2(6): 102-109.
[5] Yong Wang,Yongdong Wang,Huifang Guo,Yumin Zhou. Measuring Item Similarity Based on Increment of Diversity[J]. 数据分析与知识发现, 2018, 2(5): 70-76.
[6] Lingfeng Hua,Gaoming Yang,Xiujun Wang. Recommending Diversified News Based on User’s Locations[J]. 数据分析与知识发现, 2018, 2(5): 94-104.
[7] Yu Wang,Xiuxiu Li. Evaluating Business Reputation with E-Commerce Comments[J]. 数据分析与知识发现, 2017, 1(8): 59-67.
[8] Fuliang Xue,Junling Liu. Improving Collaborative Filtering Recommendation Based on Trust Relationship Among Users[J]. 数据分析与知识发现, 2017, 1(7): 90-99.
[9] Xingxin Qin,Rongbo Wang,Xiaoxi Huang,Zhiqun Chen. Slope One Collaborative Filtering Algorithm Based on Multi-Weights[J]. 数据分析与知识发现, 2017, 1(6): 65-71.
[10] Peng Zhu, Xiaoxiao Zhao, Wei Wu. Factors Influencing Mobile E-commerce Consumers’ Preferences: An Empirical Study[J]. 数据分析与知识发现, 2017, 1(3): 1-9.
[11] Li Daoguo,Li Lianjie,Shen Enping. New Collaborative Filtering Recommendation Algorithm Based on User Rating Time[J]. 现代图书情报技术, 2016, 32(9): 65-69.
[12] Tan Xueqing,Zhang Lei,Huang Cuicui,Luo Lin. A Collaborative Filtering and Recommendation Algorithm Using Trust of Domain-Experts and Similarity[J]. 现代图书情报技术, 2016, 32(7-8): 101-109.
[13] Wang Yong,Deng Jiangzhou,Deng Yongheng,Zhang Pu. A Collaborative Filtering Recommendation Algorithm Based on Item Probability Distribution[J]. 现代图书情报技术, 2016, 32(6): 73-79.
[14] Liu Honglian,Zhang Pengyi,Wang Jun. Multi-session Product Information Seeking Behaviors, Motivation, and Influencing Factors[J]. 现代图书情报技术, 2016, 32(4): 1-7.
[15] Ma Li. Collaborative Filtering Recommendation Method Based on User Learning Tree[J]. 现代图书情报技术, 2016, 32(4): 72-80.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn