The growing need to manage and exploit the proliferation of online data sources is opening up new opportunities for bringing people closer to the resources they need. For instance, consider a recommendation service through which researchers can receive daily pointers to journal papers in their elds of interest. We survey some of the known approaches to the problem of technical paper recommendation and ask how they can be extended to deal with multiple information sources. More speci cally, we focus on a variant of this problem { recommending conference paper submissions to reviewing committee members { which o ers us a testbed to try di erent approaches. Using WHIRL { an information integration system { we are able to implement di erent recommendation algorithms derived from information retrieval principles. We also use a novel autonomous procedure for gathering reviewer interest information from the Web. We evaluate our approach and compare it to other methods using preference data provided by members of the AAAI-98 conference reviewing committee along with data about the actual submissions.
Basu et al. REF focused on recommending conference paper submissions to reviewers based on paper abstracts and reviewer profiles that were extracted from the Web.
Technical Paper Recommendation: A Study in Combining Multiple Information Sources
Journal Of Artificial Intelligence Research, Volume 14, pages 231-252, 2001
Computer Science