Sequential recommendations in IOT scenarios with a generalized user behaviour model
Recommender Systems (RSs) are typically used to support users in finding web content of their interest. We consider here an alternative scenario: to support human decision making in the physical world. In particular, we focus on Internet of Things (IoT), where, for instance, users exploration of a sensor enabled city 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. We leverage two distinct components: a generalised user behavioural model and a complementary recommender system; here recommendations can deviate from the usual approach to directly use the learned behaviour model and suggest the most likely actions the user will take next. We also propose techniques for simulating user behaviour and analysing the collective dynamics of a population of users. Moreover, we tackle the lack of data produced by interactions of users with IoT augmented areas, by designing a simulator that can be used to collect user preferences and monitor their decisions.