Geographical POI recommendation for Internet of Things: A federated learning approach using matrix factorization

With the popularity of Internet of Things (IoT), Point-of-Interest (POI) recommendation has become an important application for location-based services (LBS). Meanwhile, there is an increasing requirement from IoT devices on the privacy of user sensitive data via wireless communications. In order to provide preferable POI recommendations while protecting user privacy of data communication in a distributed collaborative environment, this paper proposes a federated learning (FL) approach of geographical POI recommendation. The POI recommendation is formulated by an optimization problem of matrix factorization, and singular value decomposition (SVD) technique is applied for matrix decomposition. After proving the nonconvex property of the optimization problem, we further introduce stochastic gradient descent (SGD) into SVD and design an FL framework for solving the POI recommendation problem in a parallel manner. In our FL scheme, only calculated gradient information is uploaded from users to the FL server while all the users manage their rating and geographic preference data on their own devices for privacy protection during communications. Finally, real-world dataset from large-scale LBS enterprise is adopted for conducting extensive experiments, whose experimental results validate the efficacy of our approach.