Exploiting Context Graph Attention for POI Recommendation in Location-Based Social Networks

To address the limitations of existing methods, we propose a Context Graph Attention (CGA) model, which can integrate context information encoded in different context graphs with the attention mechanism for POI recommendation. CGA first uses two context-aware attention networks to learn the influence weights of different friends and neighboring POIs respectively. At the same time, it applies a dual attention network, which considers the mutual influence of context POIs for a user and the context users for a POI, to learn the influence weights of different context vertices in the user-POI context graph. A multi-layer perceptron integrates the context vectors of users and POIs for estimating the visiting probability of a user to a POI. To the best of our knowledge, this is the first work that applies the attention mechanism for POI recommendation. Extensive experiments on two public check-in data sets show that CGA can outperform the state-of-the-art methods as well as other attentive collaborative filtering methods substantially.