Generating Dual-Directed Recommendation Information from Point-of-Sales Data of a Supermarket

Authors:
Masakazu Takahashi;Toshiyuki Nakao;Kazuhiko Tsuda;Takao Terano
Affiliations:
Graduate School of Business Sciences, University of Tsukuba, Tokyo, Japan 112-0012;Dept. Computational Intelligence and Systems Science, Tokyo Institute of Technology, , Yokohama, Japan 226-8502;Graduate School of Business Sciences, University of Tsukuba, Tokyo, Japan 112-0012;Dept. Computational Intelligence and Systems Science, Tokyo Institute of Technology, , Yokohama, Japan 226-8502
Venue:
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Year:
2008

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Abstract

Even at the supermarket in Japan, it is commonly used the reward card. However, it is used for only sales expansion objectives with the twice or triple points so far. This paper proposes the methods extracting customer preference information and the characteristics of the commodity from the Point of Sales (POS) data with the reward card. One of the challenges in this paper is how to grasp not only the customer preferences but the trends of the preferences. In the conventional methods, customer preference and market information are managed with two-dimensional vectors of customer and preference category axes. In this proposed method, we add time axis to make it threedimensional vectors in order to figure out the time-series changes. With this preferences extracting algorithm, we have set up the dual-recommendation site at daikoc.net to browse the trend for both items and customers. Furthermore, we have found trend leaders among the customers, that which confirm that there is a possibility to make appropriate recommendations to the other group member based on the transitions of the trend leaders' preferences.