Abstract
This paper proposes a SOM clustering method using user’s features to classify profitable customer for recommender service in e-Commerce. In this paper, it is necessary for us to classify profitable customer with RFM (Recency, Frequency, and Monetary) score, to use the purchase data to join the customers using SOM with input vectors of different features, RFM factors in order to do recommender service in u-commerce, to reduce customers’ search effort for finding items, and to improve the rate of accuracy. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.
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References
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This is work was supported by funding of Namseoul University.
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Cho, Y.S., Moon, S.C., Ryu, K.H. (2015). SOM Clustering Method Using User’s Features to Classify Profitable Customer for Recommender Service in u-Commerce. In: Park, J., Pan, Y., Kim, C., Yang, Y. (eds) Future Information Technology - II. Lecture Notes in Electrical Engineering, vol 329. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9558-6_32
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DOI: https://doi.org/10.1007/978-94-017-9558-6_32
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