Intelligent Collaborative Information Retrieval

Abstract

The next generation of intelligent information systems will rely on cooperative agents for playing a fundamental role in actively searching and finding relevant information on behalf of their users in complex and open environments, such as the Internet. On the other hand, the relevance of such information is a user-dependent notion within the scope or context of a particular domain or topic. Previous work, mainly in information retrieval (IR), focuses on the analysis of the content by the means of keyword-based metrics. Some recent algorithms apply social or collaborative information filtering to improve the task of retrieving relevant information and for refining each agent’s particular knowledge. In this paper, we combine both approaches developing a new content-based filtering technique for learning up-to-date users. profiles that serves as basis for a novel collaborative information-filtering algorithm. We demonstrate our approach through a system called RAAP (Research Assistant Agent Project) devoted to support collaborative research by classifying domain specific information, retrieved from the Web, and recommending these “bookmarks” to other researchers with similar interests.