Sequential recommendation methods play an important role in real-world recommender systems. These systems are able to catch user preferences by taking advantage of historical records and then performing recommendations. Contrastive learning(CL) is a cutting-edge technology that can assist us in obtaining informative user representations, but these CL-based models need subtle negative sampling strategies, tedious data augmentation methods, and heavy hyper-parameters tuning work. In this paper, we introduce another way to generate better user representations and recommend more attractive items to users. Particularly, we put forward an effective \textbf{C}onsistency \textbf{C}onstraint for sequential \textbf{Rec}ommendation(C$^2$-Rec) in which only two extra training objectives are used without any structural modifications and data augmentation strategies. Substantial experiments have been conducted on three benchmark datasets and one real industrial dataset, which proves that our proposed method outperforms SOTA models substantially. Furthermore, our method needs much less training time than those CL-based models. Online AB-test on real-world recommendation systems also achieves 10.141\% improvement on the click-through rate and 10.541\% increase on the average click number per capita. The code is available at \url{https://github.com/zhengrongqin/C2-Rec}.

翻译：序列推荐方法在真实推荐系统中扮演着重要角色，这些系统通过利用历史记录捕捉用户偏好并进行推荐。对比学习（CL）是一种前沿技术，能够帮助我们获取信息丰富的用户表示，但这些基于CL的模型需要精妙的负采样策略、繁琐的数据增强方法以及大量的超参数调优工作。本文引入另一种方式，以生成更优的用户表示并向用户推荐更具吸引力的物品。具体而言，我们提出了一个用于序列推荐的高效一致性约束框架（C²-Rec），该框架仅使用两个额外训练目标，无需任何结构修改或数据增强策略。我们在三个基准数据集和一个真实工业数据集上进行了大量实验，证明所提方法显著优于现有最优模型。此外，我们的方法所需训练时间远少于基于CL的模型。在真实推荐系统上的在线AB测试中，点击率提升10.141%，人均点击次数提升10.541%。代码已开源至 \url{https://github.com/zhengrongqin/C2-Rec}。