ITEM: Improving Training and Evaluation of Message-Passing based GNNs for top-k recommendation

TL;DR

This paper introduces a ranking loss-based training approach for message-passing GNNs in top-k recommendation, improving performance and training speed over traditional proxy loss methods.

Contribution

It proposes a novel ranking loss optimization with smooth rank approximations and a personalized PageRank negative sampling strategy for GNNs in collaborative filtering.

Findings

Outperforms BPR loss across four datasets and architectures

Achieves faster training times

Enhances evaluation with an inductive user-centric protocol

Abstract

Graph Neural Networks (GNNs), especially message-passing-based models, have become prominent in top-k recommendation tasks, outperforming matrix factorization models due to their ability to efficiently aggregate information from a broader context. Although GNNs are evaluated with ranking-based metrics, e.g NDCG@k and Recall@k, they remain largely trained with proxy losses, e.g the BPR loss. In this work we explore the use of ranking loss functions to directly optimize the evaluation metrics, an area not extensively investigated in the GNN community for collaborative filtering. We take advantage of smooth approximations of the rank to facilitate end-to-end training of GNNs and propose a Personalized PageRank-based negative sampling strategy tailored for ranking loss functions. Moreover, we extend the evaluation of GNN models for top-k recommendation tasks with an inductive user-centric protocol, providing a more accurate reflection of real-world applications. Our proposed method significantly outperforms the standard BPR loss and more advanced losses across four datasets and four recent GNN architectures while also exhibiting faster training. Demonstrating the potential of ranking loss functions in improving GNN training for collaborative filtering tasks.