Dual Test-time Training for Out-of-distribution Recommender System

TL;DR

This paper introduces DT3OR, a novel test-time training framework that adapts recommender systems to shifting user and item distributions, significantly improving out-of-distribution recommendation performance.

Contribution

The paper presents the first test-time training strategy for OOD recommendation, incorporating self-distillation and contrastive tasks for effective model adaptation.

Findings

DT3OR outperforms state-of-the-art baselines on three datasets.

The framework effectively adapts to distribution shifts during testing.

Theoretical analysis supports the dual test-time training approach.

Abstract

Deep learning has been widely applied in recommender systems, which has achieved revolutionary progress recently. However, most existing learning-based methods assume that the user and item distributions remain unchanged between the training phase and the test phase. However, the distribution of user and item features can naturally shift in real-world scenarios, potentially resulting in a substantial decrease in recommendation performance. This phenomenon can be formulated as an Out-Of-Distribution (OOD) recommendation problem. To address this challenge, we propose a novel Dual Test-Time-Training framework for OOD Recommendation, termed DT3OR. In DT3OR, we incorporate a model adaptation mechanism during the test-time phase to carefully update the recommendation model, allowing the model to specially adapt to the shifting user and item features. To be specific, we propose a self-distillation task and a contrastive task to assist the model learning both the user's invariant interest preferences and the variant user/item characteristics during the test-time phase, thus facilitating a smooth adaptation to the shifting features. Furthermore, we provide theoretical analysis to support the rationale behind our dual test-time training framework. To the best of our knowledge, this paper is the first work to address OOD recommendation via a test-time-training strategy. We conduct experiments on three datasets with various backbones. Comprehensive experimental results have demonstrated the effectiveness of DT3OR compared to other state-of-the-art baselines.