Preliminary Evaluation of the Test-Time Training Layers in Recommendation System (Student Abstract)

Tianyu Zhan, Zheqi Lv, Shengyu Zhang, Jiwei Li

AAAI 2025 pp. 29554-29557

doi:10.1609/AAAI.V39I28.35323 /aaai/2025/zhan2025aaai-preliminary/

Abstract

This paper explores the application and effectiveness of TestTime Training (TTT) layers in improving the performance of recommendation systems. We developed a model, TTT4Rec, utilizing TTT-Linear as the feature extraction layer. Our tests across multiple datasets indicate that TTT4Rec, as a base model, performs comparably or even surpasses other baseline models in similar environments.

AAAI Semantic Scholar

Cite

Text

Zhan et al. "Preliminary Evaluation of the Test-Time Training Layers in Recommendation System (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35323

Markdown

[Zhan et al. "Preliminary Evaluation of the Test-Time Training Layers in Recommendation System (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/zhan2025aaai-preliminary/) doi:10.1609/AAAI.V39I28.35323

BibTeX

@inproceedings{zhan2025aaai-preliminary,
  title     = {{Preliminary Evaluation of the Test-Time Training Layers in Recommendation System (Student Abstract)}},
  author    = {Zhan, Tianyu and Lv, Zheqi and Zhang, Shengyu and Li, Jiwei},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {29554-29557},
  doi       = {10.1609/AAAI.V39I28.35323},
  url       = {https://mlanthology.org/aaai/2025/zhan2025aaai-preliminary/}
}