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An E-Commerce Recommender System using Complaint Data and Review Data

In recent years, the use of e-commerce recommender systems has become more widespread, with key applications including tracking user purchase histories, considering value estimates and product review comments, and recommending higherrated related items. However, traditional recommender systems are not consistent when recommending alternative items based on user input. Although users choose options on an existing product or service, (e.g., screen size and quality), it is still difficult to satisfy users’ requirements. To solve this problem, we propose a novel item recommender system that analyzes two kinds of data: complaint data from the Fuman Kaitori Center and reviewer comments on e-commerce. First, the system generates the negative vectors of user-checked items from complaint data and positive vectors of related item data by subtracting lower-rated reviews from higher-rated reviews. Next, the system calculates the similarities between these two vectors and determines which reviews can resolve complaints related to user-checked items. Thus, the proposed system can provide suitable substitutes for user checked items. In this paper, we describe our proposed recommendation method based on complaint data and review data, and verify its efficacy using qualitative evaluation. ACM Classification