Content Filtering of Decentralized P2P Search System Based on Heterogeneous Neural Networks Ensemble

  • Xianghua Fu
  • Boqin Feng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)


A Peer-to-Peer (P2P) based decentralized personalized information access system called PeerBridge for edge nodes of the Internet network is proposed to provide user-centered, content-sensitive, and high quality information search and discovery service from Web and P2P network timely. The general system architecture, user modeling and content filtering mechanism of PeerBridge are discussed in detail. Moreover in order to only find information which users are interested in, a new heterogeneous neural network ensemble (HNNE) classifier is presented for filtering irrelevant information, which combines several component neural networks to accomplish the same filtering task, and improves the generalization performance of a classification system. Performance evaluation in the experiments showed that PeerBridge is effective to search relevant information for individual users, and the filtering effect of the HNNE classifier is better than that of support vector machine, Naïve Bayes, and individual neural network.


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  1. 1.
    Singh, A., Srivatsa, M., Liu, L., Miller, T.: Apoidea: a Decentralized Peer-to-Peer Architecture for Crawling the World Wide Web. In: SIGIR 2003 Workshop on Distributed Information Retrieval (2003)Google Scholar
  2. 2.
    Zhou, J., Li, K., Tang, L.: Towards a Fully Distributed P2P Web Search Engine. In: 10th IEEE International Workshop on Future Trends of Distributed Computing System (2004)Google Scholar
  3. 3.
    Suel, T., Mathur, C., Wu, J.-W., Zhang, J.: ODISSEA: A Peer-to-Peer Architecture for Scalable Web Search and Information Retrieval. In: 6th International Workshop on the Web and Databases (2003)Google Scholar
  4. 4.
    Fu, X.H., Feng, B.Q., Ma, Z.F., Ming, H.: Focused Crawling Method with Online-Incremental Adaptive Learning. Journal of Jiaotong University 38, 599–602 (2004)Google Scholar
  5. 5.
    Fu, X.H., Feng, B.Q., Ma, Z.F., Ming, H.: Method of Incremental Construction of Heterogeneous Neural Network Ensemble with Negative Correlation. Journal of Jiaotong University 38, 796–799 (2004)MATHGoogle Scholar
  6. 6.
    Stoica, I., Morris, R., Karger, D., Kaashoek, M.F., Balakrishnan, H.: Chord: A Scalable Peer-To-Peer Lookup Service for Internet Application. In: SIGCOMM Annual Conference on Data Communication (2001)Google Scholar
  7. 7.
    Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 34, 1–47 (2002)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Zhou, Z.H., Wu, J.-X., Tang, W.: Ensembling Neural Networks: Many Could Be Better Than All. Artificial Intelligence 137, 239–263 (2002)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Hansen, L.K., Salamon, P.: Neural Network Ensembles. IEEE Transaction on Pattern Analysis and Machine Intelligence 12, 993–1001 (1990)CrossRefGoogle Scholar
  10. 10.
    Joachims, T.: Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: 10th European Conference on Machine Learning (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xianghua Fu
    • 1
  • Boqin Feng
    • 1
  1. 1.Department of Computer Science and TechnologyXi’an Jiaotong UniversityXi’anChina

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