Abstract
This paper proposes an 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 the recommending services 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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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., Chao, HC., Yi, G. (eds) Ubiquitous Computing Application and Wireless Sensor. Lecture Notes in Electrical Engineering, vol 331. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9618-7_21
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DOI: https://doi.org/10.1007/978-94-017-9618-7_21
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