A Novel P2P Information Clustering and Retrieval Mechanism

  • Huaxiang Zhang
  • Peide Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


Information retrieval over peer-to-peer networks is an important task. In order to avoid query message flooding and improve information retrieval performance, clustering the nodes sharing the same kind of interests is a feasible approach. An interest crawling agent utilizing an incremental learning algorithm is proposed to calculate a crawled node’s score, which is used for establishing a node cluster. An active time window is employed to accelerate the query. In order to utilize the node cluster efficiently, we present a ε -greedy query routing strategy. Experimental results show our approach performs well.


Master Node Vector Space Model Node Cluster Retrieval Accuracy Indexing Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kalogeraki, V., Gunopulos, D., Zeinalipour-Yazti, D.: A Local Search Mechanism for Node-to-Node Networks. In: Proc. of CIKM 2002, McLean VA, USA (2002)Google Scholar
  2. 2.
    Triantallou, P., Xiruhaki, C., Koubarakis, M., Ntarmos, N.: Towards high performance node-to-node content and resource sharing systems. In: Proceedings of the int. conf. on innovative data systems research(CDIR) (2003)Google Scholar
  3. 3.
    Lu, J., Callan, J.: Content-based retrieval in hybrid peer-to-peer networks. In: CIKM 2003 (November 2003)Google Scholar
  4. 4.
    Ogilvie, P., Callan, J.: Experiments using the lemure toolkit. In: Proc. of the 10th text retrieval conference (TREC-10) (2001)Google Scholar
  5. 5.
    Sripanidkulchai, K., Maggs, B., Zhang, H.: Efficient content location using interest-based locality in node-to-node systems. In: INFOCOM 2003 (2003)Google Scholar
  6. 6.
    Eisenhardt, M., Müller, W., Henrich, A.: Classifying documents by distributed p2p clustering. GI Jahrestagung (2), 286–291 (2003)Google Scholar
  7. 7.
    Fessant, L., Handurukande, S., Kermarrec, A.M., Massoulié, L.: Clustering in Peer-to-Peer File Sharing Workloads (2004)Google Scholar
  8. 8.
    Voulgaris, S., Kermarrec, A., Massoulié, L., Steen, M.V.: Exploiting semantic proximity in node-to-node content searching (2004)Google Scholar
  9. 9.
    Lu, Z., McKinley, K.S.: The effect of collection organization and query locality on information retrieval system performance and design. In: Bruce croft (ed.) Advances in information retrieval, Kluwer, New York (2000)Google Scholar
  10. 10.
    Risson, J., Moors, T.: Survey of research towards robust peer-topeer networks: search methods. Technical Report UNSW-EE-P2P-1-1, Univ. of New South Wales, Sydney, Australia (2004)Google Scholar
  11. 11.
    Can project home page,
  12. 12.
    Menczer, F., Pant, G., Srinivasan, P.: Topical web crawlers: Evaluating adaptive algorithms. ACM Transactions on Internet Technology (forthcoming, 2003), online at
  13. 13.
    Salton, G.: Automatic Information Organization and Retrieval. McGraw-Hill, New York (1968)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Huaxiang Zhang
    • 1
  • Peide Liu
    • 2
  1. 1.Dept. of Computer ScienceShandong Normal UniversityJinanChina
  2. 2.Dept. of Information MangementShandong Economic UniversityJinanChina

Personalised recommendations