Sequential recommendation tasks, which aim to predict the next item a user will interact with, typically rely on models trained solely on historical data. However, in real-world scenarios, user behavior can fluctuate in the long interaction sequences, and training data may be limited to model this dynamics. To address this, Test-Time Training (TTT) offers a novel approach by using self-supervised learning during inference to dynamically update model parameters. This allows the model to adapt to new user interactions in real-time, leading to more accurate recommendations. In this paper, we propose TTT4Rec, a sequential recommendation framework that integrates TTT to better capture dynamic user behavior. By continuously updating model parameters during inference, TTT4Rec is particularly effective in scenarios where user interaction sequences are long, training data is limited, or user behavior is highly variable. We evaluate TTT4Rec on three widely-used recommendation datasets, demonstrating that it achieves performance on par with or exceeding state-of-the-art models. The codes are available at https://github.com/ZhaoqiZachYang/TTT4Rec.

翻译：序列推荐任务旨在预测用户将要交互的下一个项目，通常依赖于仅基于历史数据训练的模型。然而，在实际场景中，用户行为可能在长交互序列中发生波动，而训练数据可能不足以建模这种动态性。为解决此问题，测试时训练（TTT）提供了一种新颖的方法，通过在推理过程中使用自监督学习来动态更新模型参数。这使得模型能够实时适应新的用户交互，从而实现更准确的推荐。本文提出TTT4Rec，一种集成TTT以更好地捕捉动态用户行为的序列推荐框架。通过在推理过程中持续更新模型参数，TTT4Rec在用户交互序列较长、训练数据有限或用户行为高度变化的场景中尤为有效。我们在三个广泛使用的推荐数据集上评估TTT4Rec，结果表明其性能达到或超越了最先进的模型。代码可在 https://github.com/ZhaoqiZachYang/TTT4Rec 获取。