KGIE: Knowledge graph convolutional network for recommender system with interactive embedding

In recent years, knowledge graphs (KGs) have gained considerable traction across various domains, especially in the realm of recommender systems, where their integration has garnered significant interest. These integrations aim to enhance the accuracy of recommendations by leveraging user-item interaction data and item attributes. However, existing methods encounter several challenges, including excessive smoothing, sparse data, redundancy, inadequate consideration of auxiliary information, and limitations in constructing deep networks. To address these challenges and enhance knowledge-graph-based recommendation methods that ignore auxiliary information and framework redundancy in neighborhood information aggregation, this study proposes a novel graph neural network recommendation model based on interactive embedding. This model capitalizes on both the KG and user-item interaction matrix to extract valuable information, refine aggregation methods, and optimize the overall performance. Specifically, user–relation interactive embedding is formed by establishing connections between users and relations using the user–item interaction matrix and KG as the foundation. This interactive embedding merges with the user through convolutional neural networks (CNNs), independently participating in the aggregation of neighborhood information to provide more contextual information for recommendation. The set of users who interact with items is extracted employing the user-item interaction matrix and creating item–user interactive embedding. It is then merged with the item using a CNN. Lastly, the final representations of the users and items for prediction are obtained. Experimental evaluations conducted on six real recommendation datasets demonstrate that our proposed model outperforms existing baselines.