  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.
