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。
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).
作者
张文轩
孙福振
王澳飞
张志伟
王绍卿
ZHANG Wenxuan;SUN Fuzhen;WANG Aofei;ZHANG Zhiwei;WANG Shaoqing(School of Computer Science and Technology,Shandong University of Technology,Zibo 255049,China)
出处
《国防科技大学学报》
北大核心
2025年第4期170-179,共10页
Journal of National University of Defense Technology
基金
国家自然科学基金资助项目(61841602)
山东省自然科学基金资助项目(ZR2020MF147)。
关键词
对比学习
自监督学习
序列推荐
数据增强
注意力机制
contrastive learning
self-supervised learning
sequential recommendation
data augmentation
attention mechanism
