Abstract
Information filtering is a technique to identify, in large collections, information that is relevant according to some criteria (e.g., a user's personal interests, or a research project objective). As such, it is a key technology for providing efficient user services in any large‐scale information infrastructure, e.g., digital libraries. To provide large‐scale information filtering services, both computational and knowledge management issues need to be addressed. A centralized (single‐agent) approach to information filtering suffers from serious drawbacks in terms of speed, accuracy, and economic considerations, and becomes unrealistic even for medium‐scale applications. In this article, we discuss two distributed (multi‐agent) information filtering approaches, that are distributed with respect to knowledge or functionality, to overcome the limitations of single‐agent centralized information filtering. Large‐scale experimental studies involving the well‐known TREC data set are also presented to illustrate the advantages of distributed filtering as well as to compare the different distributed approaches.