  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}.
