Recommendation of scholarly venues based on dynamic user interests

It is no secret that the number of scholarly events and venues available for researchers is and has been dramatically expanding. While this tremendous expansion is certainly a boon for academia as a whole, it has become increasingly difficult for many researchers to identify events and venues related to their work. Therefore, as opportunities to share scholarly work continue to expand, researchers may find themselves unable to determine effectively which venues publish data and research most in line with their scholarly interests. Not only does this constrain a researcher’s base of knowledge to build upon, it likewise limits the researcher’s ability to publish original research in appropriate venues. In this study, we present a system to recommend scholarly venues rated in terms of relevance to a given researcher’s current scholarly pursuits and interests. We collected our data from an academic social network and modeled researchers’ scholarly behavior in order to propose a new and adaptive implicit rating technique for venues. We conducted analytical experiments on this system and found that the academic social network studied can effectively recommend scholarly venues and that the proposed rating outperforms the baseline venue recommendation in terms of accuracy and ranking quality.