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.
翻译:大型电子商务网站可以收集和分析大量用户偏好和行为,从而可以向用户推荐高度信任的产品,然而,个人或非公司团体很难获得大规模用户数据,因此,我们考虑是否可以利用决策领域的知识获得用户偏好,并将其与基于内容的过滤结合起来,设计一个信息检索系统。本研究报告根据产品相似性和关于因特网产品的其他多种观点,描述了建立产品信息浏览支持系统的过程,我们介绍了拟议系统的结构,并解释了其组成单元的工作原则。最后,我们通过评估试验和问卷,展示了拟议系统的有效性。