TTT4Rec: A Test-Time Training Approach for Rapid Adaption in Sequential Recommendation

TL;DR

TTT4Rec introduces a test-time training method for sequential recommendation systems, enabling real-time model adaptation to dynamic user behaviors and limited data, thereby improving recommendation accuracy.

Contribution

The paper proposes TTT4Rec, a novel framework that integrates test-time training into sequential recommendation models for dynamic user behavior adaptation.

Findings

Outperforms existing models on three datasets.

Effective in scenarios with limited training data.

Enhances recommendation accuracy in variable user behavior contexts.

Abstract

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.