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
this https URL

我们提出了一种名为自我监督图神经网络（SelfGNN）的新型框架，用于顺序推荐。该框架通过时间间隔对短期图进行编码，并利用图神经网络（GNN）学习短期协作关系。它通过间隔融合和动态行为建模，在多个粒度级别捕捉长期用户和物品表示。我们的个性化自增强学习结构通过根据长期用户兴趣和个人稳定性减轻短期图中的噪声，增强了模型的鲁棒性。广泛的实验证明，SelfGNN 优于各种最先进的基线模型。