Sequential Recommendation is a widely studied paradigm for learning users' dynamic interests from historical interactions for predicting the next potential item. Although lots of research work has achieved remarkable progress, they are still plagued by the common issues: data sparsity of limited supervised signals and data noise of accidentally clicking. To this end, several works have attempted to address these issues, which ignored the complex association of items across several sequences. Along this line, with the aim of learning representative item embedding to alleviate this dilemma, we propose GUESR, from the view of graph contrastive learning. Specifically, we first construct the Global Item Relationship Graph (GIRG) from all interaction sequences and present the Bucket-Cluster Sampling (BCS) method to conduct the sub-graphs. Then, graph contrastive learning on this reduced graph is developed to enhance item representations with complex associations from the global view. We subsequently extend the CapsNet module with the elaborately introduced target-attention mechanism to derive users' dynamic preferences. Extensive experimental results have demonstrated our proposed GUESR could not only achieve significant improvements but also could be regarded as a general enhancement strategy to improve the performance in combination with other sequential recommendation methods.

翻译：序列推荐是一种广泛研究的范式，通过学习用户历史交互中的动态兴趣来预测下一个潜在项目。尽管大量研究工作已取得显著进展，但仍受限于监督信号稀疏和意外点击造成的数据噪声等常见问题。为此，部分研究尝试解决这些问题，但忽略了跨多个序列的项目复杂关联。基于此，为学习具有代表性的项目嵌入以缓解这一困境，我们提出GUESR，从图对比学习的视角出发。具体而言，我们首先从所有交互序列中构建全局项目关系图（GIRG），并提出桶聚类采样（BCS）方法进行子图划分。随后，在该精简图上进行图对比学习，以从全局视角增强具有复杂关联的项目表示。接着，我们扩展CapsNet模块，并引入精心设计的目标注意力机制，以推导用户的动态偏好。大量实验结果表明，我们提出的GUESR不仅能取得显著性能提升，还可作为一种通用增强策略，与其他序列推荐方法结合以改善推荐效果。