PRESS: Personalized Event Scheduling Recommender System (Demonstration)

This paper presents a personalized event scheduling recommender system, PRESS, for a large conference setting with multiple parallel tracks. PRESS is a mobile application that gathers personalized information from a user and recommends talks/demos to be attend. The input from a user include a list of keyword preferences and (optionally) preferred talks. We use the MALLET topic model package to analyze the set of conference papers and classify them based on automatically identified topics. We propose an algorithm to generate a list of recommended papers based on the user keywords and the MALLET topics. An optimization model is then applied to obtain a feasible schedule. The recommended set is matched against the selected papers by the user which we obtained from a survey conducted at AAMAS-15 in Istanbul, Turkey. We show that PRESS is able to provide reasonable accuracy, precision and recall rates. PRESS will be deployed live during AAMAS-16 in Singapore.