Product Information Browsing Support System Using Analytic Hierarchy Process

TL;DR

This paper presents a system that supports product browsing by combining decision-making domain knowledge with content-based filtering, aiming to improve user satisfaction in e-commerce environments.

Contribution

It introduces a novel architecture for a product browsing support system that leverages domain knowledge and multiple product perspectives to enhance user experience.

Findings

System effectively improves user satisfaction

Demonstrated through evaluation experiment

Validated by user questionnaire

Abstract

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.