Back-buy prediction based on TriFG
Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics
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Recommender systems can apply knowledge discovery techniques to the problem of making product recommendations. This aims to establish a customer loyalty strategy and thus to optimize the customer life time value. In this paper we propose CAPRE, a data-mining based methodology for recommender systems based on the analysis of turnover for customers of specific products. Contrary to classical recommender systems, CAPRE does not aspire to predict a customer's behavior but to influence that behavior. By measuring the action ability and profitability of customers, we have the ability to focus on customers that can afford to spend larger sums of money in the target business. CAPRE aggregates rules to extract characteristic purchasing behaviors, and then analyzes the counter-examples to detect the most actionable and profitable customers. We measure the effectiveness of CAPRE by performing a cross-validation on the Movie Lens benchmark. The methodology is applied to over 10,000 individual customers and 100,000 products for the customer relationship management of VM Matériaux company, thus assisting the salespersons' objective to increase the customer value.