Ranking information resources in peer-to-peer text retrieval: an experimental study

ABSTRACT

This paper experimentally studies approaches to the problem of ranking information resources w.r.t. user queries in peer-to-peer information retrieval. In distributed environments, for each given user query and a set of information resources that are available, we need to select the right subset of these resources to forward the query to. Here, we study the problem of pruning descriptions of resources to acceptable lengths in a peer-to-peer scenario and two approaches to overcome the mismatch problem that may arise as a consequence of the pruning, namely query expansion and learning better resource descriptions from query streams. The results show that resource descriptions can be pruned to a large extent without ill effects and that learning better descriptions from query streams works much better than query expansion.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Selection process

Additional Classification:
  C. Computer Systems Organization
  C.2 COMPUTER-COMMUNICATION NETWORKS
      C.2.4 Distributed Systems
          Subjects: Distributed applications

General Terms:
Algorithms, Experimentation

Keywords:
intelligent query routing, peer-to-peer information retrieval, profile adaptation, profiles, query expansion