DiKGRec: Generative Recommender Model with Diffusion and Knowledge Graph-Based Reasoning

Generative AI has shown remarkable advancements across various tasks, including recommender systems, where recent research leverages generative approaches to provide personalised recommendations based on user-item historical interaction data. However, the inherent sparsity of the interaction data poses a significant challenge to the advancement of generative recommender models. While some discriminative models have explored incorporating knowledge graphs (KGs) to address this issue, they often struggle with noise sensitivity, lack of explainability, and difficulties in handling cold-start scenarios, where new items with little or no historical user interaction data are involved. In this paper, we propose a novel dual-architecture generative model that intuitively integrates a diffusion model with KG-based reasoning, which reflects the propagation of user preference in a KG towards items. Our approach not only improves recommendation accuracy significantly, but also introduces explainability by leveraging the structured insights from KGs. Furthermore, the KG-based reasoning enables our model to effectively address cold-start scenarios. By utilising the semantic connections in the KG, our model can recommend these new items with confidence, overcoming a common limitation of traditional methods. We evaluate our model on three benchmark datasets, demonstrating superior performance (beat SOTA by over 10% in recall@20 in average).