论文标题
SR-GCL：基于会话的建议，具有全球环境增强对比学习的增强
SR-GCL: Session-Based Recommendation with Global Context Enhanced Augmentation in Contrastive Learning
论文作者
论文摘要
基于会话的建议旨在根据持续的会话预测用户的下一个行为。先前的作品是将会话建模为一系列项目的可变长度，并学习单个项目和汇总会话的表示。最近的研究应用了图形神经网络，具有注意机制，通过将会话建模为图形结构化数据来捕获复杂的项目过渡和依赖性。但是，他们仍然在数据和学习方法方面面临着根本的挑战，例如稀疏的监督信号和会议中的嘈杂互动，导致次优表现。在本文中，我们提出了SR-GCL，这是一个基于会话建议的新型对比学习框架。作为对比度学习的关键组成部分，我们提出了两种全球环境增强的数据增强方法，同时保持原始会话的语义。与其他最先进的方法相比，两个现实世界电子商务数据集的广泛实验结果证明了SR-GCL的优势。
Session-based recommendations aim to predict the next behavior of users based on ongoing sessions. The previous works have been modeling the session as a variable-length of a sequence of items and learning the representation of both individual items and the aggregated session. Recent research has applied graph neural networks with an attention mechanism to capture complicated item transitions and dependencies by modeling the sessions into graph-structured data. However, they still face fundamental challenges in terms of data and learning methodology such as sparse supervision signals and noisy interactions in sessions, leading to sub-optimal performance. In this paper, we propose SR-GCL, a novel contrastive learning framework for a session-based recommendation. As a crucial component of contrastive learning, we propose two global context enhanced data augmentation methods while maintaining the semantics of the original session. The extensive experiment results on two real-world E-commerce datasets demonstrate the superiority of SR-GCL as compared to other state-of-the-art methods.
