Using Ontology and Universal User Profile for Personalized Recommendation

• Main Authors: Ren-De Ou, 歐仁德
• Other Authors: Li-Hua Li
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/11909359743483955699

碩士 === 朝陽科技大學 === 資訊管理系碩士班 === 94 === Nowadays personalization services are getting popular than before. To discover customers preference, to provide needable offers or product recommendation are benefits in rising enterprise’s profits. To conduct personalized services in Internet environment, websites are acquired to establish intimate user’s profile for the most part. In addition, by collecting user’s web logs, user’s web usage behavior can be discovered and, therefore, the proper recommendation service can be achieved. It is noticed, usually a user has too many user profiles stored in many web site’s. Because of it, the problems of privacy, consistency and cold-start has been brought into existence. Therefore, scholars have proposed the technology of storing user profiles in client-side, termed “Universal User Profile.” The technology wholly collect user’s web logs and provides unique portal of privacy data authorize to promote the problems of user profile. However most users are surf on various web pages, the diversification and complexity of user’s browsing behavior has turned the discovery of user’s preference more difficult. To resolve the problems mentioned above, this research is aiming in building the personalized recommendation system by using ontology and universal user profile. Firstly, the website directory service is used as ontology to identify user’s browsing behaviors on Internet to discover user preferences. Secondly, redundant web logs are filtered out by using web usage mining, i.e., to enhance the accuracy of personalization. Finally, user’s potential preference is discovered by using the hierarchical property from user preference directory, that is, to bring out the universal user profile which match the characteristics of the user. A design called Universal User Profile Recommendation System(UUPRS) has bulit to verify the research. In UUPRS the first step is to discover user’s preference by analyzing user’s log behavior. The second step is using Web Directory Service to build ontology tree. The ontology tree is used for creating user preference tree(UPT). In this research, three methods are designed to examine the effects of recommendation. These methods are weigthed sum method, session identification method and ontology inference method. The result of the experiment evidenced that the use of UUPRS can create the accuracy of 74%. With web usage mining and ontology, the recommendation accuracy to 90%. Therefore, this research proved that the use of UUPRS is able to promote personalized service and together with the ontology method is able to improve the positive effect of personalization.