Conditional diffusion model for recommender systems

Abstract

Recommender systems are used to filter personalized information for users, as it help avoid information overload. The diffusion model is an advanced deep generative model that has been used in recommender systems due to its effectiveness in reconstructing users’ interaction vectors and predicting their preferences. The conditional diffusion model is an improvement of the diffusion model that introduces the guidance information in the reverse diffusion process, where the guidance information is usually labels or features related to the reconstructed vector. The main contribution of this article is developing an effective recommendation method based on the conditional diffusion model, which aims to introduce the user’s preference feature into the reverse diffusion process and improve the recommendation performance. For this purpose, we propose an effective strategy utilizing the user’s own interaction vectors as conditional guidance information and using neural networks as encoders. The above two approaches contribute 7.41% and 6.00% to the performance improvement, respectively. We select five datasets on movies, music, beauty, and sports products for our experiments, with sample sizes ranging from 50,000 to 500,000, and sparsity ranging from 0.05% to 3.42%. Compared to the best performance of selected baselines, our proposed model improves the Top10 metrics by 5.59% and the Top20 metrics by 4.38%. Besides, the hyper-parameters sensitivity analysis shows that the small diffusion steps and the moderate introduced noise result in good performance. Finally, we present the limitations of C-DiffRec in relationship network applications and the scalability of the model framework depth.