Multi-view Self-attention Network for Next POI Recommendation

Abstract

Next Point-of-Interest (POI) recommendation has been applied by many Internet companies to enhance user travel experience. The state-of-the-art deep learning methods in next POI recommendation advocate the self-attention mechanism to model the user long-term check-in sequence. However, the existing methods ignore the interdependence between POI and POI category in the historical interaction. The POI and POI category sequences can be regarded as multi-view information of user check-in behaviors. This paper proposes a multi-view self-attention network (MVSAN) for next POI recommendation. Firstly, MVSAN uses a self-attention layer to update the feature representation of POI and POI category respectively. Then it generates the importance of POI under the condition of the POI category through a co-attention module. To make better use of geospatial information, we design a spatial candidate set filtering module to help the model improve recommendation performance. Experiments on two real check-in datasets show that MVSAN yields outstanding improvements over the state-of-the-art models in terms of recall.