Academic Venue Recommendation Based on Refined Cross Domain

Abstract

AbstractTo submit a scientific discovery, researchers are generally overloaded with the huge number of academic venues. The concrete challenge is how to select the right academic venue that corresponds to the research area and fits the researcher’s current interests (the academic venue types, publisher, rank, location) without missing the submission deadline. This paper proposes a new approach to supply researchers with adapted and personalized academic venues that satisfy their requirements and fit with their preferences. The proposed solution combines several recommendation techniques that use different data sources, i.e., authors past publications as the target domain and authors, from the reference list, past publications as source domain as well as preferences provided by the authors (venue’s type, publisher, rank, location), to suggest situable academic venues. It is able to deal with authors changing interests by filtering out irrelevant conferences that do not respond to the researcher’s requirements. It is also useful for young researchers who have not publications yet. Experimental results demonstrate the efficiency of our proposed method.