| Summary: | 碩士 === 中原大學 === 資訊工程研究所 === 96 === In electronic commerce, recommendation systems can be used to suggest suitable products for individual customers. Usually, such systems first classify the items, then divide users into clusters according to their behaviors on the items, and finally make the recommendation based on the clustered results. The clusters are often computed when the user is online and the response time is thus critical. Therefore, this paper aims at clustering the users to fit the distribution of their actual behaviors and proposes an efficient method for it. For accuracy, we adopt the density-based instead of distance-based clustering because the latter is not suitable for clusters with irregular shapes. Moreover, we use the concept of experts to select the representative users in each cluster. For efficiency, we integrate the concept of density into a grid-based index to reduce the computation for cluster update. With this mechanism, the difficulty of costly computation in density-based clustering methods is thus alleviated. A music recommendation system has been built for evaluation. Our system can recommend a personalized music list based on user experience in listening to different kinds of music.
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