CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation

TL;DR

CT4Rec introduces a straightforward consistency training approach for sequential recommendation that enhances user representations and outperforms contrastive learning models with less complexity and training time.

Contribution

The paper proposes a simple consistency training method for sequential recommendation that avoids complex data augmentation and hyper-parameter tuning, yet achieves superior performance.

Findings

Outperforms state-of-the-art models on benchmark datasets.

Achieves significant improvements in click-through rate and user engagement metrics.

Requires less training time compared to contrastive learning-based methods.

Abstract

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}.