Causal intervention for knowledge graph denoising in recommender systems

Knowledge graph (KG) with enriched items’ related information has been widely used to alleviate the data sparsity and cold-start problems in recommender systems. However, the noise in KG that is irrelevant to a recommendation task may mislead the decision outcomes. The existing research predominantly employs data-driven modeling which uncovers the underlying patterns of the model by mining correlations within the data. This learning paradigm that lacks causality may lead to spurious associations and limit the robustness of recommendation results. To tackle this problem, this paper proposes a novel framework called knowledge graph denoising-based causal recommendation (KGDCR). In this framework, we fully combine the advantages of data-driven and model-driven modeling, and introduce a causality-driven recommendation mechanism based on causal inference. This mechanism enhances the robustness of the model by identifying the causal relationships between user behaviors and recommendation decisions. Specifically, we leverage graph attention neural networks to aggregate semantic information from the KG. Furthermore, the KGDCR captures personalized user preferences at a fine granularity by intervening in the noise. Then, we formulate a cross-view-constrained optimization problem to guide the recommendation model towards stable prediction. Experimental results demonstrate that the denoising performance and robustness of the KGDCR outperform the existing methods.