Abstract
Addressing the problems that available search engines seldom consider the personalized needs of users with low precision rate and the discrete retrieval results, an Agent-based intelligent meta-search engine model is proposed. Agent technology is used, which makes the system more intelligent. In order to achieve personalized retrieval analysis, the model uses a four-tuple user interest model and improved text classification model. A retrieval result synthesis strategy is proposed based on the factors of initial positions, related degree of retrieval queries and abstracts, and weight of individual search engines. And result consistence sorting is also realized. The experimental results show that the proposed model has a preferably performance.
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Li, Q., Sun, Y. (2012). An Agent Based Intelligent Meta Search Engine. In: Wang, F.L., Lei, J., Gong, Z., Luo, X. (eds) Web Information Systems and Mining. WISM 2012. Lecture Notes in Computer Science, vol 7529. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33469-6_71
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DOI: https://doi.org/10.1007/978-3-642-33469-6_71
Publisher Name: Springer, Berlin, Heidelberg
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