Implicit local-global feature extraction for diffusion sequence recommendation

The existing research using diffusion model for item distribution modeling is a novel and effective recommendation method. However, the user interaction sequences contain multiple implicit features that reflect user preferences, and how to use implicit features to guide the diffusion process remains to be studied. Therefore, considering the dynamics of user preferences, we conduct fine-grained modeling of diffusion recommendation process. Specifically, we firstly define a sequence feature extraction layer that utilizes multi-scale convolutional neural networks and residual long short-term memory networks to learn local-global implicit features, and obtains implicit features through a weighted fusion strategy. Subsequently, the extracted output features are used as conditional inputs for the diffusion recommendation model to guide the denoising process. Finally, the items that meet user preferences are generated through the sampling and inference process to realize the personalized recommendation task. Through experiments on three publicly available datasets, the results show that the proposed model outperforms the strong baseline model in terms of performance. In addition, we conduct hyperparameter analysis and ablation experiments to verify the impact of model components on overall performance.