Inferring win-lose product network from user behavior

Various data mining techniques to extract product relations have been examined, especially in the context of building intelligent recommender systems. Most such techniques, however, specifically examine co-occurrences of browsed or purchased products on e-commerce websites, which provide little or no useful information related to the direct relation of superiority or the factor which forms that superiority. For marketers and product managers, understanding the competitive advantages of a given product is important to consolidate their product differentiation strategies. As described in this paper, we propose a win-lose relation, a new product relation analysis method that retrieves the superiority relation between competitive products in terms of product attractiveness. Our proposed method uses the difference between user browsing and purchasing behaviors, assuming that a purchased product is superior to products that are browsed but not purchased. We also propose superiority factor analysis to examine keywords that represent the superiority factor by mining product reviews. We evaluate our methods using an actual dataset from Zexy, the largest wedding portal website in Japan. Our experimental evaluation revealed that our proposed method can estimate actual user preferences observed from a user study using only log data. Results also show that our proposed method raises the accuracy of superiority factor extraction by around 17% by considering the win-lose relation of products.