Sequential recommendation aims to leverage users' historical behaviors to
predict their next interaction. Existing works have not yet addressed two main
challenges in sequential recommendation. First, user behaviors in their rich
historical sequences are often implicit and noisy preference signals, they
cannot sufficiently reflect users' actual preferences. In addition, users'
dynamic preferences often change rapidly over time, and hence it is difficult
to capture user patterns in their historical sequences. In this work, we
propose a graph neural network model called SURGE (short for SeqUential
Recommendation with Graph neural nEtworks) to address these two issues.
Specifically, SURGE integrates different types of preferences in long-term user
behaviors into clusters in the graph by re-constructing loose item sequences
into tight item-item interest graphs based on metric learning. This helps
explicitly distinguish users' core interests, by forming dense clusters in the
interest graph. Then, we perform cluster-aware and query-aware graph
convolutional propagation and graph pooling on the constructed graph. It
dynamically fuses and extracts users' current activated core interests from
noisy user behavior sequences. We conduct extensive experiments on both public
and proprietary industrial datasets. Experimental results demonstrate
significant performance gains of our proposed method compared to
state-of-the-art methods. Further studies on sequence length confirm that our
method can model long behavioral sequences effectively and efficiently.

本文提出了一个基于图神经网络模型的序列推荐方法 SURGE (Short for SeqUential Recommendation with Graph neural nEtworks)，通过度量学习将松散的物品序列重构成紧密的物品 - 物品兴趣图，并对构建的图进行集群感知和查询感知的图卷积传播和图池化操作，从而实现从用户的历史行为序列中动态融合和提取其当前激活的核心兴趣。实验证明，相比最先进的方法，该方法在公共和专有数据集上都达到了显著的性能提升，并且在序列长度方面也表现出了高效和有效的建模能力。