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.

翻译：序列推荐旨在利用用户的历史行为预测其下一次交互。现有工作尚未解决序列推荐中的两个主要挑战。首先，用户丰富历史序列中的行为通常是隐式且带有噪声的偏好信号，无法充分反映用户的真实偏好。此外，用户的动态偏好往往随时间快速变化，因此难以从历史序列中捕捉用户模式。本文提出一种名为SURGE（SeqUential Recommendation with Graph neural nEtworks的缩写）的图神经网络模型来解决这两个问题。具体而言，SURGE通过基于度量学习将松散的物品序列重构为紧密的物品-物品兴趣图，将用户长期行为中的不同类型偏好整合到图中的聚类中。这有助于通过形成兴趣图中的密集聚类，清晰地区分用户的核心兴趣。随后，我们在构建的图上执行聚类感知和查询感知的图卷积传播与图池化操作。该操作动态融合并从噪声用户行为序列中提取用户当前激活的核心兴趣。我们在公开数据集和专有工业数据集上进行了大量实验。实验结果表明，与最先进方法相比，我们提出的方法取得了显著的性能提升。针对序列长度的进一步研究证实，该方法能够高效地对长行为序列进行建模。