Next Item and Interval Prediction of New Users Using Meta-Learning on Dynamic Network

Recommendation systems play a pivotal role in diverse real-world scenarios, offering personalized suggestions to users. Despite their significance, the cold-start problem poses a formidable challenge for both conventional recommendation systems and sequential recommendations. The entry of new users or items into the system inhibits accurate recommendations due to the lack of prior interactions. To tackle this issue, researchers have delved into employing meta-learning techniques. However, predicting the time interval of interactions for new users remains a persistent challenge. This paper presents an innovative approach to forecasting the next item and the associated time interval of new user and item interactions. Our method leverages meta-learning techniques within the context of a dynamic graph structure. It showcases superior performance when compared to previous methods using three benchmark datasets, effectively addressing the cold-start problem. The instructive experiments underscore the efficacy of our proposed method in handling the next item and time interval prediction, thereby contributing to the advancement of sequential recommendation systems.