Two characteristics of location-based services are mobile trajectories and the ability to facilitate social networking. The recording of trajectory data contributes valuable resources towards understanding users' geographical movement behaviors. Social networking is possible when users are able to quickly connect to anyone nearby. A social network with location based services is known as location-based social network (LBSN). As shown in Cho et al. [2013] , locations that are frequently visited by socially related persons tend to be correlated, which indicates the close association between social connections and trajectory behaviors of users in LBSNs. To better analyze and mine LBSN data, we need to have a comprehensive view of each of these two aspects, i.e., the mobile trajectory data and the social network. Specifically, we present a novel neural network model that can jointly model both social networks and mobile trajectories. 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: a networking representation can be derived for a user. The key to our model lies in generating mobile trajectories. Second, we consider four factors that influence the generation process of mobile trajectories: user visit preference, influence of friends, short-term sequential contexts, and long-term sequential contexts. To characterize the last two contexts, we employ the RNN and GRU models to capture the sequential relatedness in mobile trajectories at the short or long term levels. Finally, the two components are tied by sharing the user network representations. Experimental results on two important applications demonstrate the effectiveness of our model. In particular, the improvement over baselines is more significant when either network structure or trajectory data is sparse. . 2017. A neural network approach to jointly modeling social networks and mobile trajectories. 1 https://en.wikipedia.org/wiki/Gowalla. 2 https://en.wikipedia.org/wiki/Brightkite. 3 Note that an online social relationship does not necessarily indicate an offline friendship in real life. a trajectory is usually modeled as a sequence of check-in records. A commonly used way to incorporate social connections into an application system (e.g., recommender systems) is to adopt the regularization techniques by assuming that the links convey user similarity. In this way, the social connections are exploited as the side information but not characterized by a joint data model, and the model performance highly relies on the "homophily principle" that like associates with like. In this article, we take the initiative to jointly model social networks and mobile trajectories using a neural network approach. Our approach is inspired by the recent progress in deep learning. Compared to other methods, neural network models can serve as an effective and general function approximation mechanism that is able to capture complicated data characteristics [Mittal 2016] . Specifically, recent studies have shown the superiority of neural network models on network and sequential data. First, several pioneering studies tried to embed vertices of a network into low-dimensional vector spaces [Tang and Liu 2011; Perozzi et al. 2014; Tang et al. 2015b ], called networking embedding. With such a low-dimensional dense vector, it can alleviate the data sparsity from which a sparse network representation suffers. Second, neural network models are powerful computational data models that are able to capture and represent complex input/output relationships. In particular, several neural network models for processing sequential
For example, Yang et al. REF employed the RNN and GRU (Gated Recurrent Unit) models to capture the sequence relatedness in mobile trajectories at shortterm or long-term.
A Neural Network Approach to Jointly Modeling Social Networks and Mobile Trajectories
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Computer Science