STS: A Context-Aware Mobile Recommender System for Places of Interest
Matthias Braunhofer, Mehdi Elahi, Francesco Ricci⋆ · 2014
Abstract. In this demo paper we present a novel context-aware mo-bile 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. Per-sonality 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. 1