Feature-decorrelation adaptive contrastive learning for knowledge-aware recommendation

Abstract

Knowledge graphs (KGs) are utilized in recommendation systems due to their rich semantic information, with graph neural networks (GNNs) employed to capture multi-hop knowledge and relationships within KGs. However, GNN-based methods, with their iterative linear propagation and the complexity of entity features in KGs, face two significant challenges: (1) The linear iterative aggregation of high-order complex attribute entities can lead to feature loss and distortion in knowledge representation, thereby hindering effective feature modeling; and (2) High-order irrelevant knowledge along the propagation path can cause deviations in recommendation topics. To address these issues, we propose a feature-decorrelation adaptive contrastive learning method for knowledge-aware recommendations. Specifically, we investigate the impact of inter-feature correlations and propose a simple yet effective constraint method to learn representations for downstream tasks. Additionally, we propose an adaptive knowledge refinement method to extract effective high-order semantics from KGs, thereby generating augmented views. Finally, We propose a contrastive learning approach to keep the learned representation focused on the recommended topic and adaptively reduce the negative impact of irrelevant knowledge. We conduct experiments on four public datasets, including Movielens and Yelp, to validate the effectiveness of the proposed method. In particular, our feature decorrelation method demonstrates significant effectiveness in knowledge-aware recommender systems based on GNNs. Our code is available at https://github.com/CTimeris/FACLK.