TTT4Rec: A Test-Time Training Approach for Rapid Adaption in Sequential Recommendation
This addresses the challenge of fluctuating user behavior in sequential recommendation for real-world applications, though it is incremental as it applies an existing test-time training approach to a new domain.
The paper tackles the problem of adapting sequential recommendation models to dynamic user behavior by proposing TTT4Rec, which uses test-time training to update parameters during inference, achieving performance on par with or exceeding state-of-the-art models on three datasets.
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.