Sequential recommendation effectively addresses information overload by modeling users' temporal and sequential interaction patterns. To overcome the limitations of supervision signals, recent approaches have adopted self-supervised learning techniques in recommender systems. However, there are still two critical challenges that remain unsolved. Firstly, existing sequential models primarily focus on long-term modeling of individual interaction sequences, overlooking the valuable short-term collaborative relationships among the behaviors of different users. Secondly, real-world data often contain noise, particularly in users' short-term behaviors, which can arise from temporary intents or misclicks. Such noise negatively impacts the accuracy of both graph and sequence models, further complicating the modeling process. To address these challenges, we propose a novel framework called Self-Supervised Graph Neural Network (SelfGNN) for sequential recommendation. The SelfGNN framework encodes short-term graphs based on time intervals and utilizes Graph Neural Networks (GNNs) to learn short-term collaborative relationships. It captures long-term user and item representations at multiple granularity levels through interval fusion and dynamic behavior modeling. Importantly, our personalized self-augmented learning structure enhances model robustness by mitigating noise in short-term graphs based on long-term user interests and personal stability. Extensive experiments conducted on four real-world datasets demonstrate that SelfGNN outperforms various state-of-the-art baselines. Our model implementation codes are available at https://github.com/HKUDS/SelfGNN.

翻译：序列推荐通过建模用户的时间与序列交互模式，有效缓解信息过载问题。为克服监督信号的局限性，近期研究已在推荐系统中采用自监督学习技术。然而，仍存在两个关键挑战亟待解决：其一，现有序列模型主要关注个体交互序列的长期建模，忽视了不同用户行为间有价值的短期协同关系；其二，现实数据常包含噪声，尤其在用户短期行为中，这类噪声可能源于临时意图或误点击，会对图模型与序列模型的准确性产生负面影响，进一步增加建模复杂度。为应对这些挑战，本文提出一种名为自监督图神经网络的新型序列推荐框架SelfGNN。该框架基于时间间隔编码短期图，并利用图神经网络学习短期协同关系；通过间隔融合与动态行为建模，在多层次粒度上捕获长期用户与物品表征。值得注意的是，本文提出的个性化自增强学习结构能够依据长期用户兴趣与个人稳定性缓解短期图中的噪声，从而增强模型鲁棒性。在四个真实数据集上的大量实验表明，SelfGNN性能优于多种前沿基线模型。我们的模型实现代码已发布于https://github.com/HKUDS/SelfGNN。