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http://dx.doi.org/10.9708/jksci.2014.19.2.193

Clustering Analysis by Customer Feature based on SOM for Predicting Purchase Pattern in Recommendation System  

Cho, Young Sung (Dept. of Computer Science, Dongyang mirae University)
Moon, Song Chul (Dept. of Computer Science, Namseoul University)
Ryu, Keun Ho (School of Electrical and Computer Science, Chungbuk National University)
Publication Information
Journal of the Korea Society of Computer and Information / v.19, no.2, 2014 , pp. 193-200 More about this Journal
Abstract
Due to the advent of ubiquitous computing environment, it is becoming a part of our common life style. And tremendous information is cumulated rapidly. In these trends, it is becoming a very important technology to find out exact information in a large data to present users. Collaborative filtering is the method based on other users' preferences, can not only reflect exact attributes of user but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, we propose clustering method by user's features based on SOM for predicting purchase pattern in u-Commerce. it is necessary for us to make the cluster with similarity by user's features to be able to reflect attributes of the customer information in order to find the items with same propensity in the cluster rapidly. The proposed makes the task of clustering to apply the variable of featured vector for the user's information and RFM factors based on purchase history data. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.
Keywords
Segmentation Method; SOM(Self-Organizing Map); Recommendation System;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 Wei, J.-T., Lin, S.-Y. and Wu, H.-H.: The review of the application of RFM model. In: African Journal of Business Management, Vol. 4, No. 19, pp. 4199-42060, (2010)
2 Cho, Y.S., Moon, S.C., Noh, S.C., Ryu, K.H.: mplementation of Personalized recommendation System using k-means Clustering of Item Category based on RFM. In: 2012 IEEE International Conference on Management of Innovation &Technology Publication (June 2012)
3 Park, H.B, Y.S., Moon, S.C., Ko, H.H.: Clustering Method of Weighted Preference Using K-means Algorithm and Bayesian Network for Recommendation System. In: Journal of Information Technology & Management, Vol. 20, No. 3, pp. 219-230, (2013)
4 Verdu. S. V. : Classification, Filtering, and Identification of Electrical Customer Load Patterns Through the Use of Self-Organizing Maps. In : IEEE Trans. Power System. Vol. 21, No.4, pp. 1672-168 (2006).   DOI   ScienceOn
5 Jun S.H.: Technology Marketing using PCA , SOM, and STP Strategy Modeling. In: IJCSI(International Journal of Computer Science Issues), Vol. 8, Issue 1, January 2011
6 Kate A. Smith, Jatinder N. D Gupta.: Neural Networks in Business: Techniques and Applications. In: The IDEA GROUP PUBLISHING (2001).
7 T. Kohonen, Self-Organizing Maps. In: Springer (2000)
8 Cho, Y.S., Moon, S.C. Ryu, K.H.: SOM Clustering Method based on RFM Analysis for Predicting Customer Purchase Pattern in u-Commerce. In: KSCI , The Korea Society of Computer and Information Summer Conference 2013, ( Jul, 2013)
9 Herlocker, J.L., Kosran, J.A., Borchers, A., Riedl, J.: An Algorithm Framework for Performing Collaborative Filtering.In: Proceedings of the 1999 Conference on Research and Development in Information Research and Development in Information Retrival (1999)
10 Cho, Y.S., Moon, S.C., Jeong, S.P., Oh, I.B., Ryu, K.H.: Clustering Method using Item Preference based on RFM for Recommendation System in u-Commerce. In: (eds.) Ubiquitous Information Technologies and Applications. LNEE, vol. 214, pp. 353-362. Springer, Hidelberg (2012)