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:序列建议是一个广泛研究的范例,用于从历史互动中了解用户对预测下一个潜在项目的动态兴趣,尽管许多研究工作取得了显著进展,但它们仍然受到常见问题的影响:受监督的信号有限,不小心点击的数据噪音也有限;为此,一些工作试图解决这些问题,忽视了项目在多个序列中的复杂关联。根据这一思路,我们从图表对比性学习的角度,建议GUESR学习具有代表性的项目,以减轻这一困境。具体地说,我们首先从所有互动序列中构建全球项目关系图(GIRG),并介绍Bucket-Cluster抽样(BCS)进行分图的方法。然后,就这一缩小的图表进行对比性学习,以加强与全球复杂关联的项目表述。我们随后将CapsNet模块与精心引入的目标保留机制扩展,以获得用户的动态偏好。广泛的实验结果表明,我们提议的GUESR不仅可以实现重大改进,而且还可以被视为一项总体改进战略,以改进与其他建议组合的绩效。</s>