A POI Recommendation Model with Temporal-Regional Based Graph Representation Learning

POI recommendation aims to predict the locations that users may be interested in at next time based on the user's historical check-in sequence information. It is a key task to improve customer experience and business operations, which has aroused widespread interest in academia and industry. But it is still challenging due to the diversity of human activities and the sparseness of the available check-in records. In order to cope with these challenges, The paper proposes a recommendation method based on graph representation learning: Temporal-Regional based Graph Convolutional Network (TRGCN) to further improve the accuracy of prediction. The model first builds a multi-graph representation based on the user's check-in record, and at the same time integrates contextual information such as time period and region into the graph. After that, the model learns the representation of each node at a specific time through the graph neural network. In addition, we apply different score functions to evaluate users' preferences for POIs and regions. The experiment performance of TRGCN proves the effectiveness of constructing a multi-graph structure of user check-in records based on spatio-temporal context to learn the representation of graph nodes. In addition, there is a strong correlation between sequence data. Experiments have also proved the effectiveness of recurrent neural network (or its variants) in processing sequence data. At the same time, the attention mechanism will learn more important information from the sequence to further improve model performance