| Summary: | 碩士 === 中原大學 === 資訊管理研究所 === 98 === In the era of information explosion, a lot of information surrounds our daily life. It is important that helping people to filter out unnecessary data can improve their performance on obtaining appropriate information. Therefore, this study adopts some user profile information to construct user preference model. This research also develops a classified method and a simulated tool to recommend items and contents for users.
Firstly, the proposed method uses k-means clustering method to group users according to their personal attributes. Secondly, we use neural networks to simulate user’s preference. On the other hand, fuzzy method considers the preferences of users to recommend items by searching through neighborhood. Finally, this system combines k-means clustering, neural networks, and fuzzy methods to recommended items for users.
To resolve the new user problem of traditional recommendation methods, the proposed method uses the rating results of existing neighbors in the same cluster to construct the preference network of new users to predict user’s rating results. Comparing the experimental results obtained from neural networks, decision tree, and association rules, the proposed method can achieve better prediction accuracy and increase the quality of recommendation results.
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