STS: A Context-Aware Mobile Recommender System for Places of Interest
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In this demo paper we present a novel context-aware mobile recommender system for places of interest (POIs). Unlike existing systems, which learn users' preferences solely from their past ratings, it considers also their personality -using the Five Factor Model. Personality is acquired by asking users to complete a brief and entertaining questionnaire as part of the registration process, and is then exploited in: (1) an active learning module that actively acquires ratings-in-context for POIs that users are likely to have experienced, hence reducing the stress and annoyance to rate (or skip rating) items that the users don't know; and (2) in the recommendation model that builds up on matrix factorization and therefore can be trained even if the users haven't rated any items yet.