Fusing User Preferences and Spatiotemporal Information for Sequential Recommendation

Abstract

At present, the research on sequence recommendation mainly focuses on using the historical interaction data between users and items to mine their relationship, so as to predict the next interaction between users and items, then generate the personalized recommendation. Spatiotemporal information is very important to further improve the accuracy and quality of recommendation, but the existing sequence recommendation models are mainly based on recurrent neural network (RNN), and pay less attention to spatiotemporal information. Most of the recommendation models are still in the early stage of merging spatiotemporal context information, and the processing effect of long sequence data is not ideal. a sequential recommendation model integrating user preferences and spatiotemporal information is proposed. The model captures user item long-term preferences through spatiotemporal GRU algorithm and user item short-term preferences through attention mechanism. Finally, the learned long-term and short-term preference features and user portrait features are combined to predict the next recommendation location. The experimental results on two real data sets Foursquare and Brightkite show that the proposed model performs better than state-of-the-arts in three evaluation indicators HR@K, NDCG@K and MAP@K.