Abstract
An Adaptive and Active Computing Paradigm (AACP) for long-term users to get personalized information services in heterogeneous environment is proposed to provide user-centered, push-based high quality information service timely in a proper way, the motivation of which is generalized as R4 Service: the Right information serves the Right person at the Right time in the Right way. Formalized algorithms of adaptive user profile management, active monitoring and delivery mechanism, and adaptive retrieval algorithm are discussed in details, in which Support Vector Machines is adopted for collaborate retrieval and content-based adaptation, which overcomes the demerits of using collaborative or content-based algorithm independently, and improves the precision and recall in a large degree. Performance evaluations showed the proposed paradigm in this paper was effective, stable and feasible for large-scale users to gain fresh information instead of polling from kinds of information sources.
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Ma, Z., Feng, B. (2004). Support Vector Machines Learning for Web-Based Adaptive and Active Information Retrieval. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds) Advanced Web Technologies and Applications. APWeb 2004. Lecture Notes in Computer Science, vol 3007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24655-8_10
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DOI: https://doi.org/10.1007/978-3-540-24655-8_10
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