Tangible decision-making in sensors augmented spaces
Recommender Systems (RSs) are web tools aimed at easing users’ online decision-making. Here we propose a complementary scenario: supporting (tangible) decision-making in the physical space. In particular, we propose a novel RS technology that harness data coming from a sensor augmented environment, e.g., a Smart City. In such setting, users’ movements can be tracked and the knowledge of their choices (visit to points of interest, POIs) can be used to generate recommendations for not yet visited POIs. The proposed technique overcome the inability of current RSs to generalise the preferences directly derived from the user’s observed behaviour by decoupling the learning of the user behaviour (predicted choices) from the recommender model (recommended choices). In our approach we apply clustering to users’ observed sequences of choices (i.e., POI visit trajectories) in order to identify like-behaving users and then we learn the optimal user behaviour model for each cluster. Then, by harnessing the learned optimal behaviour model we generate novel and relevant recommendations, which provide useful information in addition to choices that the user will make without any recommendation (predicted choices). In this paper we summarise the proposed RS technology; we describe its performance across different dimensions in an offline test and a users study by comparing the proposed technique with session-aware nearest neighbour based baselines (SKNN). The offline analysis results show that our approach suggests items that are novel and increases the user’s satisfaction (high reward), whereas the SKNN approaches are good at predicting the exact user behaviour. Interestingly, the online results show that the proposed approach excels in what a (tourism) RS should do: suggesting items that the user is unaware of and also relevant.