英文摘要 |
In the era of information overload, information retrieval technologies can solve the problem of information overload and extract valuable information efficiently from database. Information retrieval technologies are widely applied in many information technology fields such as: search engine, data mining, especially in Electronic Commerce recommender systems. In recent years, by the booming development in the Electronic Commerce, recommender system design has been utilized in Electronic Commerce websites to improve customer satisfaction and loyalty. However, there are two problems which have not been completely solved in traditional recommender systems: (l) the collaborative filtering recommendation approaches will be non-functional, because there is insufficient information in new products or related community. (2) The content-based recommendation approaches will be useless when users can not precisely point out their interesting and needs for products. This paper proposes a recommender system based on integrated technique of fuzzy weight and information retrieval to enhance the traditional recommender systems. By using linguistic expressions to define good attributes, the proposed system can make users find out their target goods without the priority knowledge of these goods. There are three weight operators provided in the proposed system: (1) fuzzy OWA operator, (2) fuzzy normalization operator, and (3) preference operator based on experienced users. With these weight operators, users can adjust the attribute weights more flexibly to make the searching results more reasonable to users, and, therefore, make the proposed recommender system more effectively in recommending products. Based on the proposed method, we developed a web-based prototype of digital camera recommender system. From the verification results for the prototype, there are three findings provided: (l) The fuzzy OWA operators perform very proper recommender results for users; (2) The fuzzy normalized weight operators offer more flexible query conditions for users, and represent the user concern for good attributes more impersonal. (3) The preference operator based on experienced users wills helpful recommender results by using the suggestion of the community. However, from the comparison result of three different operators, it indicates that recommender results, recommended from the fuzzy OWA weight operator, comprise the outputs from the other two operators, and, therefore, the proposed system provides better recommended results with higher coverage . |