Recommending Relevant Papers to Conference Participants: a Deep Learning Driven Content-based Approach

We introduce a novel system for personalized recommendations to conference attendees, to highlight the papers in the program that best match the attendee’s interests. To this end, we extend traditional structure-agnostic recommender system techniques through the use of deep learning, to exploit the rich semantic and topological information given by the abstracts of the papers and the citation relationship. The ultimate goal is twofold: i) to help attendees single out from a rich program the papers they most likely would like to see presented, and ii) to perform a tailored advertisement of an upcoming event to past attendees by catching their attention with specific contributions in the program of the conference.