Dynamic Graph Multi-granularity Attribute Scene Evolution Sequence Recommendation

Abstract

The recommendation based on dynamic graph sequences aims to reveal complex evolutionary patterns in user-item interactions. Existing methods make predictions by encoding attribute contents through similarity but lack dynamic modeling of fine-grained attribute scenarios, resulting in a deviation in user interest representation. To address above issues, we propose a novel Dynamic Graph multi-granularity Attribute Scene evolution sequence Recommendation (DGASR) to enhance content features and reduce user interest bias by finer granularity modeling dynamic attributes. Firstly, we design an attribute-aware reconstruction module to model attribute interest distribution to reconstruct attributes and graphs. Subsequently, we design an attribute-aware long-short term module. It enhances long-term evolution characteristics of user behavior under attribute scene changes and constrains the consistency of users’ short-term interest distribution, achieving dynamic modeling of behavioral preferences under attribute scene distribution. Finally, DGASR achieves state-of-the-art results on three benchmark datasets, significantly outperforming several typical cold-start methods.