A points of interest recommendation framework based on effective representation of heterogeneous nodes in the Internet of Things

Abstract

Location-based social networks are a special kind of the Internet of Things in which users and points of interest (POIs) are heterogeneous network nodes. When matching POIs for users, traditional recommendation methods either map users to POI space or map POIs to user space. This direct mapping of the heterogeneous space introduces some noise to the node representation. The sparse interaction between users and POIs makes it difficult to effectively distinguish the representations of similar heterogeneous nodes. Based on the above problems, this paper proposes a recommendation framework that projects heterogeneous network nodes: users and POIs into a unified representation space. Obtain the matching results of nodes in the new problem space and effectively solve the problem of unified representation of heterogeneous nodes in the Internet of Things. The setting of the intermediate latent space can distinguish the representations of similar behavior nodes as much as possible and obtain more accurate recommendation results. We conduct extensive experiments on a four real-world datasets which show that framework has superior performance compared to the state-of-the-art framework for POI recommendation.