Extraction method of typical purchase patterns based on motif analysis of directed graphs

As online stores continue to expand their businesses, a huge number of customer purchase data can be obtained. When users purchase a product, almost all users tend to post reviews on it. Therefore review data can be treated as propinquity of purchase data. In this paper, we propose a novel method that extracts typical purchase patterns based on motif analysis of a directed graph constructed from review history data. We first construct a directed graph called a purchase history graph (PHG), where a node stands for an item and a directional edge is added between successively purchased two items in chronological order. Second we decompose all of the item nodes of PHG into weakly connected components (WCCs) and expect that each WCC consists of items that are sold by the same store. For each WCC, to extract frequently appearing local edge structures, we enumerate the number of 3-node motif patterns, which is a well-known notion in complex network science. These only express theoretic patterns; the actual typical ones are slightly more complicated. Thus, we construct motif vectors, which stand for how many individual patterns are contained in each WCC. Finally, we divide all of the WCCs into K clusters based on the similarity of the motif vectors. In the above procedure, we extract the typical purchase patterns of users. From our experimental evaluation using real review dataset, we confirm the validity of each step of our proposed method and discuss the results obtained from it.