Distributionally Robust Graph Out-of-Distribution Recommendation via Diffusion Model

TL;DR

This paper introduces DRGO, a diffusion-based graph recommendation model with entropy regularization, designed to improve out-of-distribution generalization by mitigating noisy sample effects and providing theoretical guarantees.

Contribution

The paper proposes a novel distributionally robust graph recommendation model that incorporates diffusion and entropy regularization to enhance OOD performance and reduce noise influence.

Findings

DRGO outperforms baseline methods on four datasets under distribution shifts.

Theoretical analysis confirms DRGO's generalization error bounds.

Diffusion and entropy regularization effectively mitigate noisy sample effects.

Abstract

The distributionally robust optimization (DRO)-based graph neural network methods improve recommendation systems' out-of-distribution (OOD) generalization by optimizing the model's worst-case performance. However, these studies fail to consider the impact of noisy samples in the training data, which results in diminished generalization capabilities and lower accuracy. Through experimental and theoretical analysis, this paper reveals that current DRO-based graph recommendation methods assign greater weight to noise distribution, leading to model parameter learning being dominated by it. When the model overly focuses on fitting noise samples in the training data, it may learn irrelevant or meaningless features that cannot be generalized to OOD data. To address this challenge, we design a Distributionally Robust Graph model for OOD recommendation (DRGO). Specifically, our method first employs a simple and effective diffusion paradigm to alleviate the noisy effect in the latent space. Additionally, an entropy regularization term is introduced in the DRO objective function to avoid extreme sample weights in the worst-case distribution. Finally, we provide a theoretical proof of the generalization error bound of DRGO as well as a theoretical analysis of how our approach mitigates noisy sample effects, which helps to better understand the proposed framework from a theoretical perspective. We conduct extensive experiments on four datasets to evaluate the effectiveness of our framework against three typical distribution shifts, and the results demonstrate its superiority in both independently and identically distributed distributions (IID) and OOD.