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Both knowledge graphs and user-item interaction graphs are frequently used inrecommender systems due to their ability to provide rich information formodeling users and items. However, existing studies often focused on one ofthese sources (either the knowledge graph or the user-item interaction graph),resulting in underutilization of the benefits that can be obtained byintegrating both sources of information. In this paper, we propose DEKGCI, anovel double-sided recommendation model. In DEKGCI, we use the high-ordercollaborative signals from the user-item interaction graph to enrich the userrepresentations on the user side. Additionally, we utilize the high-orderstructural and semantic information from the knowledge graph to enrich the itemrepresentations on the item side. DEKGCI simultaneously learns the user anditem representations to effectively capture the joint interactions betweenusers and items. Three real-world datasets are adopted in the experiments toevaluate DEKGCI's performance, and experimental results demonstrate its higheffectiveness compared to seven state-of-the-art baselines in terms of AUC andACC.

DEKGCI: A double-sided recommendation model for integrating knowledge graph and user-item interaction graph