Abstract: With the dynamic change of user behavior preference,traditional sequential recommendation methods face the challenge of difficult to capture the change of user intention.In order to solve this problem,this study proposes guided diffusion sequence recommendation method(GDRec),which aims to achieve accurate capture of the user’s current intention by embedding the target item representation into the diffusion model.Specifically,the GDRec model includes the following key components:a sequence encoder,a cross-attention conditional denoising decoder,and a cross-divergence objective.The sequence encoder gradually generates the user preference representation to capture the dynamic relationship between the historical sequence and the current target.The cross-attention conditional denoising decoder removes the noise in the embedded representation and improves the prediction accuracy of the next target item.The cross-divergence objective,on the other hand,empowers the model with ranking capabilities,ensuring high quality representations,and embedding target item representations to guide the diffusion process.Finally,a large number of experiments on Amazon Office and Tools datasets prove that GDRec is superior to the existing advanced methods in multiple evaluation indicators,showing its superior performance in sequence recommendation tasks.In addition,ablation experiments and hyperparameter analysis further verify the effectiveness and stability of the model.

Key words: Sequential recommendation, Guided diffusion, Information embedding, User intent