Adaptivity through Unobstrusive Learning

In this paper, we present an approach for learning interest profiles implicitly from positive user observations only. This approach eliminates the need to prompt users for ratings, or to somewhat artificially infer negative evidences, which arises when traditional learning algorithms are used. We developed a methodology for learning explicit user profiles and recommending interesting objects. This highly dynamic process, which calculates the personalized recommendations in real-time, has been deployed in ELFI, a web-based system that provides information about research grants and is used by more than 1000 users in German research organizations who monitor and/or advise on extra-mural funding opportunities.

• Type: Journal
• Year: 2002
• Where: KI
• Authors: Ingo Schwab, Alfred Kobsa
• Keywords: Artificial Intelligence, Explicit User Profiles, KI 2002, Negative Evidences, Traditional Learning Algorithms