Agents that reduce work and information overload
Communications of the ACM
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
On-line Algorithms in Machine Learning
Developments from a June 1996 seminar on Online algorithms: the state of the art
Syskill & webert: Identifying interesting web sites
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
CoWing: A Collaborative Bookmark Management System
CIA '01 Proceedings of the 5th International Workshop on Cooperative Information Agents V
Novel personal and group-based trust models in collaborative filtering for document recommendation
Information Sciences: an International Journal
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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.