Abstract
We here focus on Points of Interest (POIs) Recommender Systems (RSs), aimed at helping users visiting a city to discover new and relevant POIs. RSs are often assessed in offline settings, hence, measuring the system’s precision in predicting previously observed user behaviour. However, when deployed, the system produced recommendations are often of limited use, because they lack novelty. We conjecture that this phenomenon is primarily due to the limited capability of RSs in extracting from the observed behaviour general characteristics of POIs that are relevant for different classes of users (tourist types). We compare an Inverse Reinforcement Learning (IRL) based RS algorithm with more traditional Nearest Neighbour and Popularity-based ones. Through an offline evaluation, we show that the nearest neighbour and popularity-based RSs excel in precision (offline) and are perceived as not novel by users of a live-user study. On the contrary, despite a lower offline precision, the IRL-based RS, which learns the preferences of tourists for POIs characteristics, can give a better support to a tourist.