Dual Policy Learning for Aggregation Optimization in Graph Neural Network-based Recommender Systems

by   Heesoo Jung, et al.

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 implementation code is available at https://github.com/steve30572/DPAO/.


page 1

page 2

page 3

page 4


DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation

Graph Neural Network (GNN) based recommender systems have been attractin...

DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN

In the information explosion era, recommender systems (RSs) are widely s...

Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems

Graph neural networks (GNNs) have achieved remarkable success in recomme...

Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

Recommender system is one of the most important information services on ...

MVIN: Learning Multiview Items for Recommendation

Researchers have begun to utilize heterogeneous knowledge graphs (KGs) a...

Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation

The multi-criteria (MC) recommender system, which leverages MC rating in...

DiPS: Differentiable Policy for Sketching in Recommender Systems

In sequential recommender system applications, it is important to develo...

Please sign up or login with your details

Forgot password? Click here to reset