Content-based collaborative information filtering: Actively learning to classify and recommend documents

Abstract

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. Whereas relevant can be defined solely for a specific user, and under the context of a particular domain or topic. On the other hand shared “social” information can be used 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' profile 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 researcher with similar research interests.