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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Integrating Collaborate and Content-Based Filtering for Personalized Information Recommendation
verfasst von : Zhiyun Xin, Jizhong Zhao, Ming Gu, Jiaguang Sun
Erschienen in: Computational Intelligence and Security
Verlag: Springer Berlin Heidelberg
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To achieve high quality of push-based information service, in this paper, collaborative filtering and content-based adaptability approaches are surveyed for user-centered personalized information, then based on the above method, we proposed a mixed two-phased recommendation algorithm for high-quality information recommendation, upon which performance evaluations showed that the mixed algorithm is more efficient than pure content-based or collaborative filtering methods, for pure of either approaches is not so efficient for the lack of enough information need information. And moreover we found with large amount registered users, it is necessary and important for the system to serve users in a group mode, which involved merged retrieval issues.