Learning Profiles for Heterogeneous Distributed Information Sources

Abstract

This paper experimentally studies approaches to the problem of describing heterogeneous information sources in distributed environments. In particular, we consider a scenario where a large number of end users can share and retrieve text documents over a peer-to-peer network. Descriptions (or profiles) of peers are useful in a number of applications, such as query routing, overlay construction and expert search. The approach proposed in this paper introduces a new learning method that boosts the weight of query terms in a peer's profile when the peer provides useful documents w.r.t. a given query. Experimental results show high potential for this method. Therefore, various extensions are proposed that involve more user interaction.