Combining Reinforcement Learning and Spatial Proximity Exploration for New User and New POI Recommendations

Abstract

Tourism Recommender Systems (TRSs) are unable to properly suggest new points of interest (POIs) to new users, i.e., to solve the combined new user and new item problem. To address this limitation we introduce a Reinforcement Learning TRS, which is called QEXP, and relies on a POI visits behaviour model mined from logs of POI visits data. This data is combined with general knowledge about the spatial range of tourists’ movements in a destination to generate recommendations. QEXP can recommend new POIs possessing the features of POIs experienced by tourists in the past, while favouring the exploration of POIs in the proximity of the target tourist position. We compare QEXP with four state-of-the-art POI RSs and we show that it can successfully tame the new user and new item problems. QEXP can also mitigate the concentration and popularity biases of the compared RSs and can recommend diverse and geographically dispersed POIs.