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

TL;DR

This paper investigates the use of Test-Time Training layers in recommendation systems, demonstrating that the proposed TTT4Rec model can enhance performance and compete with existing baselines.

Contribution

Introduces TTT4Rec, a novel recommendation model utilizing TTT layers, showing promising results across multiple datasets.

Findings

TTT4Rec performs comparably or better than baseline models.

Test-Time Training layers can improve recommendation system performance.

The model is effective across various datasets.

Abstract

This paper explores the application and effectiveness of Test-Time 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.