Sequential recommendation methods are increasingly important in cutting-edge recommender systems. Through leveraging historical records, the systems can capture user interests and perform recommendations accordingly. State-of-the-art sequential recommendation models proposed very recently combine contrastive learning techniques for obtaining high-quality user representations. Though effective and performing well, the models based on contrastive learning require careful selection of data augmentation methods and pretext tasks, efficient negative sampling strategies, and massive hyper-parameters validation. In this paper, we propose an ultra-simple alternative for obtaining better user representations and improving sequential recommendation performance. Specifically, we present a simple yet effective \textbf{C}onsistency \textbf{T}raining method for sequential \textbf{Rec}ommendation (CT4Rec) in which only two extra training objectives are utilized without any structural modifications and data augmentation. Experiments on three benchmark datasets and one large newly crawled industrial corpus demonstrate that our proposed method outperforms SOTA models by a large margin and with much less training time than these based on contrastive learning. Online evaluation on real-world content recommendation system also achieves 2.717\% improvement on the click-through rate and 3.679\% increase on the average click number per capita. Further exploration reveals that such a simple method has great potential for CTR prediction. Our code is available at \url{https://github.com/ct4rec/CT4Rec.git}.

翻译：序列推荐方法在尖端推荐系统中日益重要。通过利用历史记录，系统能够捕捉用户兴趣并据此进行推荐。最新提出的先进序列推荐模型结合对比学习技术，以获得高质量的用户表征。尽管基于对比学习的模型效果显著、性能优异，但其需要精心选择数据增强方法和前置任务、高效的负采样策略，以及大规模的超参数验证。本文提出一种超简便的替代方案，用于获取更优的用户表征并提升序列推荐性能。具体而言，我们提出了一种简单而有效的面向序列推荐的**表**征**一**致性**训**练方法（CT4Rec），该方法仅使用两个额外训练目标，无需任何结构性修改或数据增强。在三个基准数据集和一个新采集的大规模工业语料库上的实验表明，与基于对比学习的模型相比，我们提出的方法以大幅度优势超越现有最优模型，且训练时间大幅减少。在真实世界内容推荐系统的在线评估中，该方法实现了点击率提升2.717%，人均点击次数增加3.679%。进一步探索揭示，这种简单方法在点击率预测领域具有巨大潜力。我们的代码已开源至 \url{https://github.com/ct4rec/CT4Rec.git}。