在推荐系统中引入测试时训练层，提升模型表现

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

• 用TTT-Linear做特征提取，实现测试时动态调整
• 多数据集验证下性能优于或媲美现有基线模型
• 适合关注实时优化与泛化能力的推荐系统研究者

本文探讨了测试时训练（Test-Time Training, TTT）层在推荐系统中的应用与效果。我们构建了一个名为TTT4Rec的模型，采用TTT-Linear作为特征提取层。在多个数据集上的实验表明，TTT4Rec作为基础模型，在相似环境下表现与其它基线模型相当，甚至更优。

原文摘要 · Abstract (English)

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.

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