Product Information Browsing Support System Using Analytic Hierarchy Process
This work addresses the challenge of personalized product recommendations for users without access to big data, though it appears incremental in its approach.
The study tackled the problem of product recommendation for individuals or small groups lacking large-scale user data by designing a system that combines decision-making domain knowledge with content-based filtering, resulting in a product information browsing support system with high user satisfaction.
Large-scale e-commerce sites can collect and analyze a large number of user preferences and behaviors, and thus can recommend highly trusted products to users. However, it is very difficult for individuals or non-corporate groups to obtain large-scale user data. Therefore, we consider whether knowledge of the decision-making domain can be used to obtain user preferences and combine it with content-based filtering to design an information retrieval system. This study describes the process of building a product information browsing support system with high satisfaction based on product similarity and multiple other perspectives about products on the Internet. We present the architecture of the proposed system and explain the working principle of its constituent modules. Finally, we demonstrate the effectiveness of the proposed system through an evaluation experiment and a questionnaire.