【摘要】 针对序列推荐任务中对比学习模型生成自监督信号质量不足的问题，提出组合枚举时间间隔对比学习序列推荐模型。通过时间间隔扰动的数据增强操作，以生成保留时序信息的增强序列。为构建多视图增强序列对，提出组合枚举策略以最大化地融合用户行为与时间间隔信息。模型采用多头注意力机制对用户行为序列进行编码，并通过多任务联合训练方式优化自监督信号来提升模型性能。所提模型适用于数据稀疏性高、交互行为不均匀的场景，有效解决自监督信号建模难题。在三个真实数据集上的实验结果表明，该模型在命中率(hit ratio, HR)和归一化折损累计增益(normalized discounted cumulative gain, NDCG)指标上均优于当前最先进的对比学习模型。更多还原

【Abstract】 To address the problem of inadequate self-supervised signal quality in contrastive learning models for sequential recommendation tasks, a combinatorial enumeration and time-interval contrastive learning for sequential recommendation model was proposed. The model generated enhanced sequences which preserved temporal information through time-interval perturbation-based data augmentation. A combinatorial enumeration strategy was introduced to integrate user behavior and time-interval information, constructing multi-view augmented sequence pairs. The model employed a multi-head attention mechanism to encode user behavior sequences and optimized self-supervised signals through multi-task joint training, which improved model performance. The proposed model is well-suited for scenarios with high data sparsity and uneven interaction behaviors, effectively addressing challenges in self-supervised signal modeling. Experimental results on three real-world datasets demonstrate that the model outperforms the current state-of-the-art contrastive learning models in terms of HR(hit ratio) and NDCG(normalized discounted cumulative gain).更多还原