让推荐模型在推理时实时学习，快速适应用户行为变化。
TTT4Rec: A Test-Time Training Approach for Rapid Adaption in Sequential Recommendation
- 推理时通过自监督学习动态更新参数，无需重新训练。
- 在长序列、数据少或行为多变场景下表现优于现有模型。
- 适合需要快速响应用户兴趣变化的推荐系统应用。
序列推荐旨在预测用户下一步将交互的项目，传统方法仅依赖历史数据训练模型。然而在实际场景中，用户行为在长时间交互序列中可能发生变化，且训练数据往往有限，难以建模这种动态性。为此，测试时训练（Test-Time Training, TTT）提出在推理阶段利用自监督学习动态更新模型参数，使模型能实时适应新用户交互，提升推荐准确性。本文提出 TTT4Rec，一种融合 TTT 的序列推荐框架，通过在推理过程中持续更新模型参数，有效捕捉动态用户行为。该方法在三个广泛使用的推荐数据集上验证，性能达到或超过当前最优模型。代码已开源：https://github.com/ZhaoqiZachYang/TTT4Rec。
原文摘要 · Abstract (English)
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.
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