Enhancing Sequential Recommendation with Global Diffusion

Abstract

Existing sequential recommendation models are mostly based on sequential models, which can be misled by inconsistent items in the local sequence. This study proposes GlobalDiff, a plug-and-play framework to enhance the performance of sequential models by utilizing a diffusion model to restore the global non-sequential data structure of the item universe and compensate for the local sequential context. Several novel techniques are proposed, including training construction, guided reverse approximator, and inference ensemble, to seamlessly integrate the diffusion model with the sequential model. Extensive experiments on various datasets demonstrate that GlobalDiff can enhance advanced sequential models by an average improvement of 9.67%.