Network Embedding for Recommendation Systems on LBSNs

Abstract

The accelerated growth of mobile trajectories in location-based services brings valuable data resources to understand users’ moving behaviors. Apart from recording the trajectory data, another major characteristic of these location-based services is that they also allow the users to connect whomever they like or are interested in. A combination of social networking and location-based services is called as location-based social networks (LBSN). Existing studies indicates a close association between social connections and trajectory behaviors of users in LBSNs. In order to better analyze and mine LBSN data, we jointly model social networks and mobile trajectories with the help of network embedding techniques, which serves as the key part of the entire algorithm. Our model consists of two components: the construction of social networks and the generation of mobile trajectories. First, we adopt a network embedding method for the construction of social networks. Second, we consider four factors that influence the generation process of mobile trajectories, namely user visit preference, influence of friends, short-term sequential contexts, and long-term sequential contexts. Finally, the two components are tied by sharing the user network embeddings. Experimental results on location and friend recommendation demonstrate the effectiveness of our model.