A dynamic preference recommendation model based on spatiotemporal knowledge graphs

Recommender systems are of increasing importance owing to the growth of social networks and the complexity of user behavior, and cater to the personalized needs of users. To improve recommendation performance, several methods have emerged and made a combination of knowledge graphs and recommender systems. However, the majority of approaches faces issues like overlooking spatiotemporal features and lacking dynamic modeling. The former restricts the flexibility of recommendations, while the latter renders recommendations unable to adapt to the changing interests of users. To overcome these limitations, a novel dynamic preference recommendation model based on spatiotemporal knowledge graphs (DRSKG), which captures preferences dynamically, is proposed in this paper. Constructed by knowledge graphs, the model integrates spatiotemporal features and takes into account the dynamic preferences of users across various temporal, spatial, and situational contexts. Therefore, DRSKG not only describes the spatiotemporal characteristics of user behaviors more accurately but also models the evolution of dynamic preferences in spatiotemporal changes. Massive experiments demonstrate that the proposed model exhibits significant recommendation enhancement compared with the traditional one, achieving up to 7% and 5% improvements in terms of Precision and Recall metrics, respectively.