Sequential Recommendation (SR) has received increasing attention due to its ability to capture user dynamic preferences. Recently, Contrastive Learning (CL) provides an effective approach for sequential recommendation by learning invariance from different views of an input. However, most existing data or model augmentation methods may destroy semantic sequential interaction characteristics and often rely on the hand-crafted property of their contrastive view-generation strategies. In this paper, we propose a Meta-optimized Seq2Seq Generator and Contrastive Learning (Meta-SGCL) for sequential recommendation, which applies the meta-optimized two-step training strategy to adaptive generate contrastive views. Specifically, Meta-SGCL first introduces a simple yet effective augmentation method called Sequence-to-Sequence (Seq2Seq) generator, which treats the Variational AutoEncoders (VAE) as the view generator and can constitute contrastive views while preserving the original sequence's semantics. Next, the model employs a meta-optimized two-step training strategy, which aims to adaptively generate contrastive views without relying on manually designed view-generation techniques. Finally, we evaluate our proposed method Meta-SGCL using three public real-world datasets. Compared with the state-of-the-art methods, our experimental results demonstrate the effectiveness of our model and the code is available.

翻译：序列推荐（SR）因其捕捉用户动态偏好的能力而受到日益关注。近年来，对比学习（CL）通过学习输入不同视角下的不变性，为序列推荐提供了有效方法。然而，现有的大多数数据或模型增强方法可能破坏序列交互的语义特征，且通常依赖手工设计的对比视图生成策略。本文提出一种元优化的序列到序列生成器与对比学习（Meta-SGCL）方法用于序列推荐，该方法采用元优化的两步训练策略自适应地生成对比视图。具体而言，Meta-SGCL首先引入一种简单有效的增强方法——序列到序列（Seq2Seq）生成器，将变分自编码器（VAE）作为视图生成器，可在保留原始序列语义的同时构建对比视图。其次，模型采用元优化的两步训练策略，旨在无需依赖人工设计的视图生成技术即可自适应地生成对比视图。最后，我们在三个公开的真实数据集上评估了所提出的Meta-SGCL方法。与最先进方法相比，实验结果验证了模型的有效性，且相关代码已公开。