Journal of Software, Vol 6, No 4 (2011), 732-739, Apr 2011
doi:10.4304/jsw.6.4.732-739

A Personalized Collaborative Filtering Recommendation Using Association Rules Mining and Self-Organizing Map

Hongwu Ye

Abstract


With the development of the Internet, the problem of information overload is becoming increasing serious. People all have experienced the feeling of being overwhelmed by the number of new books, articles, and proceedings coming out each year. Many researchers pay more attention on building a proper tool which can help users obtain personalized resources. Personalized recommendation systems are one such software tool used to help users obtain recommendations for unseen items based on their preferences. The commonly used personalized recommendation system methods are content-based filtering, collaborative filtering, and association rules mining. Unfortunately, each method has its drawbacks. This paper presented a personalized collaborative filtering recommendation method combining the association rules mining and self-organizing map. It used the association rules mining to fill the vacant where necessary. Then, it employs clustering function of self-organizing map to form nearest neighbors of the target item and it produces prediction of the target user to the target item using item-based collaborative filtering. The recommendation method combining association rules mining and collaborative filtering can alleviate the data sparsity problem in the recommender systems.


Keywords


personalized service; recommender systems; association rules mining; collaborative filtering; mean absolute error

References


[1] Songjie Gong, Employing User Attribute and Item Attribute to Enhance the Collaborative Filtering Recommendation, Journal of Software, Volume 4, Number 8, October 2009, pp: 883-890.

[2] Yi-Fan Wang, Yu-Liang Chuang, Mei-Hua Hsu, Huan- Chao Keh. A personalized recommender system for the cosmetic business. Expert Systems with Applications 26 (2004) 427–434.

[3] Songjie Gong, A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering, Journal of Software, Volume 5, Number 7, July 2010, pp: 745-752.

[4] LI Pingxiang, CHEN Jiangping, BIAN Fuling, A Developed Algorithm of Apriori Based on Association Analysis, Geo-spatial Information Science, Volume 7, Issue 2, 2004

[5] TAN Ying, YIN Guofu, LI Guibing, CHEN Jianying, Mining Compatibility Rules from Irregular Chinese Traditional Medicine Database by Apriori Agorithm. Journal of Southwest Jiaotong University (English Edition) Vol.15, No.4, 2007

[6] Songjie Gong, Personalized Recommendation System Based on Association Rules Mining and Collaborative Filtering, Applied Mechanics and Materials, Volume 39, pp:540-544.

[7] Yu Li, Liu Lu, Li Xuefeng, A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce, Expert Systems with Applications 28 (2005) 67–77.
doi:10.1016/j.eswa.2004.08.013

[8] George Lekakos, George M. Giaglis, A hybrid approach for improving predictive accuracy of collaborative filtering algorithms, User Model User-Adap Inter (2007) 17:5–40.
doi:10.1007/s11257-006-9019-0

[9] Breese J, Hecherman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI’98). 1998. 43~52.

[10] Goldberg D, Nichols D, Oki BM, Terry D. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 1992,35(12):61~70.
doi:10.1145/138859.138867

[11] MICHAEL J. PAZZANI, A Framework for Collaborative, Content-Based and Demographic Filtering, Artificial Intelligence Review 13: 393–408, 1999.
doi:10.1023/A:1006544522159

[12] George Lekakos & Petros Caravelas, A hybrid approach for movie recommendation, Multimed Tools Appl (2008) 36:55–70
doi:10.1007/s11042-006-0082-7

[13] Justin Basilico,Thomas Hofmann, A Joint Framework for Collaborative and Content Filtering, SIGIR’04, July 25–29, 2004

[14] N. S. PAPASPYROU, C. E. SGOUROPOULOU and E. S. SKORDALAKIS, A Model of Collaborating Agents for Content-Based Electronic Document Filtering, Journal of Intelligent and Robotic Systems 26: 199–213, 1999.
doi:10.1023/A:1008155903288

[15] Byeong Man Kim & Qing Li & Chang Seok Park & Si Gwan Kim & Ju Yeon Kim, A new approach for combining content-based and collaborative filters, J Intell Inf Syst (2006) 27: 79–91
doi:10.1007/s10844-006-8771-2

[16] Cunningham, S., Bergen, H., & Grout, V., A Note onContent-Based Collaborative Filtering of Music, IADIS 5th-8th October (2006).

[17] Yi-Fan Wang, Yu-Liang Chuang, Mei-Hua Hsu, Huan- Chao Keh, A personalized recommender system for the cosmetic business, Expert Systems with Applications 26 (2004) 427–434

[18] Rong Jin • Luo Si • Chengxiang Zhai, A study of mixture models for collaborative filtering, Inf Retrieval (2006) 9:357–382
doi:10.1007/s10791-006-4651-1

[19] JON HERLOCKER, JOSEPH A. KONSTAN, JOHN RIEDL, An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms, Information Retrieval, 5, 287–310, 2002
doi:10.1023/A:1020443909834

[20] Schiaffino, S., & Amandi, A., Building an expert travel agent as a software agent, Expert Systems with Applications (2008), doi:10.1016/j.eswa.2007.11.032

[21] José de J. Pérez-Alcázar, Maritza L. Calderón-Benavides, Cristina N. González-Caro, Towards an Information Filtering System in the Web Integrating Collaborative and Content Based Techniques, Proceedings of the First Conference on Latin American Web Congress, p.222, November 10-12, 2003

[22] Jong-Seok Lee, Sigurdur Olafsson, Two-way cooperative prediction for collaborative filtering recommendations, Expert Systems with Applications: An International Journal Volume 36, Issue 3 April 2009

[23] J. Basilico and T. Hofmann. Unifying collaborative and content-based filtering. the21st International Conference on Machine Learning (ICML), 2004.

[24] S.S. Weng, B.S. Lin, and W.T. Chen, “Using ContextualInformation and Multidimensional Approach forRecommendation,” Expert Systems with Applications, 36(2),2009, pp. 1268-1279
doi:10.1016/j.eswa.2007.11.056

[25] Jon Kleinberg, Mark Sandler, Using mixture models for annual ACM symposium on Theory of computing, June 13-16, 2004, Chicago, IL, USA

[26] ZHOU ShaoHua, FU Lue, LIANG BaoLiu, Clustering analysis of ancient celadon based on SOM neural network, Science in China Series E: Technological Sciences, 2008, 51(7):999-1007.

[27] WANG Ling, MU Zhi-Chun,GUO Hui, Combining Selforganizing Feature Map with Support Vector Regression Based on Expert System,ACTA AUTOMATICA SINICA, 2005, 31(4):612-619.

[28] Jong-Seok Lee, Chi-Hyuck Jun, Jaewook Lee, Sooyoung Kim, Classification-based collaborative filtering using market basket data, Expert Systems with Applications 29 (2005) 700–704.
doi:10.1016/j.eswa.2005.04.037

[29] Hyung Jun Ahn, A new similarity measure for collaborative filtering to alleviate the new user coldstarting problem, Information Sciences 178 (2008) 37-51.
doi:10.1016/j.ins.2007.07.024

[30] Songjie Gong, An Efficient Collaborative Recommendation Algorithm Based on Item Clustering, Lecture notes in electrical engineering, Volume 72, pp:381-387.

[31] Gao Fengrong, Xing Chunxiao, Du Xiaoyong, Wang Shan, Personalized Service System Based on Hybrid Filtering for Digital Library, Tsinghua Science and Technology, Volume 12, Number 1, February 2007,1-8.

[32] M.G. Vozalis, K.G. Margaritis, Using SVD and demographic data for the enhancement of generalized Collaborative Filtering, Information Sciences 177 (2007) 3017–3037.
doi:10.1016/j.ins.2007.02.036

[33] Songjie Gong, An Enhanced Similarity Measure Used in Personalized Recommendation Algorithms, Advanced Materials Research, Volume 159, pp:671-675.

[34] George Lekakos, George M. Giaglis, Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors, Interacting with Computers 18 (2006) 410–431.
doi:10.1016/j.intcom.2005.11.004


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