  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.
