Personalized information agents can help overcome some of the limitations of communal Web information sources such as portals and search engines. Two important components of these agents are: user profiles and information filtering or gathering services. Ideally, these components can be separated so that a single user profile can be leveraged for a variety of information services. Toward that end, we are building an information agent called SurfAgent; in previous studies, we have developed and tested methods for automatically learning a user profile [22]. In this paper, we evaluate alternative methods for recommending new documents to a user by generating queries from the user profile and submitting them to a popular search engine. Our study focuses on three questions: How do different algorithms for query generation perform relative to each other? Is positive relevance feedback adequate to support the task? Can a user profile be learned independent of the service? We found that three...
Gabriel Somlo, Adele E. Howe