Current sequential recommender systems are proposed to tackle the dynamic user preference learning with various neural techniques, such as Transformer and Graph Neural Networks (GNNs). However, inference from the highly sparse user behavior data may hinder the representation ability of sequential pattern encoding. To address the label shortage issue, contrastive learning (CL) methods are proposed recently to perform data augmentation in two fashions: (i) randomly corrupting the sequence data (e.g. stochastic masking, reordering); (ii) aligning representations across pre-defined contrastive views. Although effective, we argue that current CL-based methods have limitations in addressing popularity bias and disentangling of user conformity and real interest. In this paper, we propose a new Debiased Contrastive learning paradigm for Recommendation (DCRec) that unifies sequential pattern encoding with global collaborative relation modeling through adaptive conformity-aware augmentation. This solution is designed to tackle the popularity bias issue in recommendation systems. Our debiased contrastive learning framework effectively captures both the patterns of item transitions within sequences and the dependencies between users across sequences. Our experiments on various real-world datasets have demonstrated that DCRec significantly outperforms state-of-the-art baselines, indicating its efficacy for recommendation. To facilitate reproducibility of our results, we make our implementation of DCRec publicly available at: https://github.com/HKUDS/DCRec.

翻译：当前的序列推荐系统旨在通过各种神经技术（如Transformer和图神经网络）处理动态用户偏好学习。然而，从高度稀疏的用户行为数据中进行推断可能阻碍序列模式编码的表征能力。为解决标签稀缺问题，近期提出了对比学习方法，通过两种方式进行数据增强：（i）随机破坏序列数据（例如随机掩码、重排序）；（ii）在预定义的对比视图间对齐表征。尽管有效，但我们认为当前基于对比学习的方法在解决流行度偏差以及解耦用户从众行为与真实兴趣方面存在局限性。本文提出了一种新的去偏对比学习推荐范式（DCRec），通过自适应从众感知增强统一了序列模式编码与全局协作关系建模。该方案旨在解决推荐系统中的流行度偏差问题。我们的去偏对比学习框架有效捕获了序列内项目转移模式以及跨序列的用户间依赖关系。在多个真实世界数据集上的实验表明，DCRec显著优于最先进的基线方法，证明了其在推荐中的有效性。为促进结果的可复现性，我们将DCRec的实现公开于：https://github.com/HKUDS/DCRec。