PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation

TL;DR

PANE-GNN is a novel graph neural network model that integrates positive and negative user feedback for improved personalized recommendations, employing separate embeddings and contrastive training to effectively utilize all feedback types.

Contribution

The paper introduces PANE-GNN, which unifies positive and negative feedback in GNNs for recommendation, using distinct message passing and contrastive learning to enhance recommendation accuracy.

Findings

Outperforms state-of-the-art methods on four real-world datasets

Effectively captures user preferences and dispreferences

Enhances recommendation quality by leveraging negative feedback

Abstract

Recommender systems play a crucial role in addressing the issue of information overload by delivering personalized recommendations to users. In recent years, there has been a growing interest in leveraging graph neural networks (GNNs) for recommender systems, capitalizing on advancements in graph representation learning. These GNN-based models primarily focus on analyzing users' positive feedback while overlooking the valuable insights provided by their negative feedback. In this paper, we propose PANE-GNN, an innovative recommendation model that unifies Positive And Negative Edges in Graph Neural Networks for recommendation. By incorporating user preferences and dispreferences, our approach enhances the capability of recommender systems to offer personalized suggestions. PANE-GNN first partitions the raw rating graph into two distinct bipartite graphs based on positive and negative feedback. Subsequently, we employ two separate embeddings, the interest embedding and the disinterest embedding, to capture users' likes and dislikes, respectively. To facilitate effective information propagation, we design distinct message-passing mechanisms for positive and negative feedback. Furthermore, we introduce a distortion to the negative graph, which exclusively consists of negative feedback edges, for contrastive training. This distortion plays a crucial role in effectively denoising the negative feedback. The experimental results provide compelling evidence that PANE-GNN surpasses the existing state-of-the-art benchmark methods across four real-world datasets. These datasets include three commonly used recommender system datasets and one open-source short video recommendation dataset.