Point-of-interest recommendation method based on temporal knowledge graph

Abstract

The disclosure discloses a point-of-interest recommendation method based on a temporal knowledge graph. Based on the historical behavior trajectory of the user and multimodal information, the disclosure constructs a dynamic temporal knowledge graph and a static group knowledge graph, which are used to learn interest preferences of the user that change over time and stable features that do not change over time, respectively. At the same time, the disclosure uses deep learning methods to build a point-of-interest recommendation model, which can extract a point-of-interest fusion feature representation and a user fusion feature representation from the two knowledge graphs in combination with a user review sentiment embedding sequence to accurately predict a point of interest most likely to be visited by the target user at a next moment. The disclosure has the characteristics of high precision and strong scalability, and can provide support for personalized user behavior trajectory prediction.