DEKGCI: A double-sided recommendation model for integrating knowledge graph and user-item interaction graph
This addresses the need for more accurate recommendations by better modeling user-item interactions, though it appears incremental as it builds on existing graph-based methods.
The paper tackled the problem of underutilizing both knowledge graphs and user-item interaction graphs in recommender systems by proposing DEKGCI, a double-sided model that integrates these sources, resulting in high effectiveness demonstrated on three real-world datasets with improvements in AUC and ACC compared to seven state-of-the-art baselines.
Both knowledge graphs and user-item interaction graphs are frequently used in recommender systems due to their ability to provide rich information for modeling users and items. However, existing studies often focused on one of these sources (either the knowledge graph or the user-item interaction graph), resulting in underutilization of the benefits that can be obtained by integrating both sources of information. In this paper, we propose DEKGCI, a novel double-sided recommendation model. In DEKGCI, we use the high-order collaborative signals from the user-item interaction graph to enrich the user representations on the user side. Additionally, we utilize the high-order structural and semantic information from the knowledge graph to enrich the item representations on the item side. DEKGCI simultaneously learns the user and item representations to effectively capture the joint interactions between users and items. Three real-world datasets are adopted in the experiments to evaluate DEKGCI's performance, and experimental results demonstrate its high effectiveness compared to seven state-of-the-art baselines in terms of AUC and ACC.