[Abstract]

Graph Neural Networks (GNNs) provide powerful representations for recommendation tasks. GNN-based recommendation systems capture the complex high-order connectivity between users and items by aggregating information from distant neighbors and can improve the performance of recommender systems. Recently, Knowledge Graphs (KGs) have also been incorporated into the user-item interaction graph to provide more abundant contextual information; they are exploited to address cold-start problems and enable more explainable aggregation in GNN-based recommender systems (GNN-Rs). However, due to the heterogeneous nature of users and items, developing an effective aggregation strategy that works across multiple GNN-Rs, such as LightGCN and KGAT, remains a challenge. In this paper, we propose a novel reinforcement learning-based message passing framework for recommender systems, which we call DPAO (Dual Policy framework for Aggregation Optimization). This framework adaptively determines high-order connectivity to aggregate users and items using dual policy learning. Dual policy learning leverages two Deep-Q-Network models to exploit the user- and item-aware feedback from a GNN-R and boost the performance of the target GNN-R. Our proposed framework was evaluated with both non-KG-based and KG-based GNN-R models on six real-world datasets, and their results show that our proposed framework significantly enhances the recent base model, improving nDCG and Recall by up to 63.7% and 42.9%, respectively.

[Biography]

Hogun Park is an Assistant Professor in the Department of Computer Science Engineering at Sungkyunkwan University (SKKU). His research interests include relational machine learning, explainable AI (XAI), and their applications. Prior to joining SKKU, he worked at the Korea Institute of Science and Technology (KIST) from 2008 to 2013, and for IBM Research-Almaden in 2018 and 2019. Park received his Ph.D. degree in Computer Science from Purdue University in 2020. He has published over 30 fully refereed papers in international journals and conferences in the area of machine learning and data mining.