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Context-Aware Recommendations Using Mobile P2P

In recent years, there has been an increasing research attention towards Context-Aware Recommender Systems (CARS) for mobile users. The main motivation is that, by considering the current context of the mobile user, more relevant suggestions can be provided. However, further research is required to enable the effective deployment of mobile CARS. In this paper, we study the possibility to implement mobile CARS by using a pure mobile P2P (Peer-to-Peer) approach, where no centralized database or server exists. Instead, the mobile devices of the users propagate rating information in an opportunistic way, when they become neighbors from a communication point of view. We study the problem by considering a specific use case scenario: the recommendation of items to observe in a museum. Besides, we exploit a synthetic generator of datasets for the evaluation of CARS, called DataGenCARS, to build the recommendation scenarios based on real and synthetic data.