| Summary: | 碩士 === 國立臺灣師範大學 === 資訊教育研究所 === 90 === Most previous works on recommendation systems of web pages were designed based on collaborative filtering according to the clusters of user browsing behavior. In these approaches, a user only belongs to certain one cluster. If most users have multiple kinds of browsing interests, the number of users in the same cluster will be small and the information used for recommendation is limited. In addition, the information of users who have partially similar behavior is not considered. In this thesis, the strategies for constructing a query and recommendation system of web pages are proposed. First, the query keywords, browsed web pages, and user feedback values are extracted from web logs to be query transactions. A clustering algorithm is proposed to find the clusters of queries and related web pages, called the clusters of query interest , from the query transactions. A user who has multiple kinds of query interests can belong to more than one cluster. Then user query transactions are partitioned based on the clusters of query interest. In each partition, the association rules of queries and web pages are mined, where the support and confidence of rules are computed based on feedback values of users. According to the mined information, two main functions are provided in the system. A member user can ask a recommendation request. Based on clusters of query interest contained in the user profile, the highly associated web pages are recommended. On the other hand, an anonymous user can ask a query recommendation request to the system by giving query keywords. According to the cluster of query interest that the query keywords belong to, the highly associated web pages are returned as query results. Therefore, the query results will be more simplified and meet the requirements of most users.
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