抄録
In this paper, we propose an approach for classifying customers in retail stores into given types according to their shopping paths, each of which is a sequence of sections visited by the corresponding customer and is gathered by an RFID tag. The approach vectorizes a sequence of sections; that is, the approach splits such a sequence into tuples of sections, then sums up occurring counts of those tuples. This vectorization is based on the hypothesis that a customer's type has relation to sub-sequences of sections in his/her shopping path and a conjecture that customers' types can be attributed to co-occurrences of such sub-sequences. After vectorization, the proposed approach applies a general discrimination method to such vectors of equal length.
In computational illustrations, the principal component regression is selected as a representative of general discrimination methods and is applied to shopping paths collected in an existing retail store so as to predict whether a customer purchases items much than average or not. Computational results display the effectiveness of the proposed approach as higher forecast accuracies than known works.
