Combinatorial enumeration and time-interval contrastive learning for sequential recommendation
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School of Computer Science and Technology, Shandong University of Technology, Zibo 255049 , China

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TP391.3

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    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).

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张文轩, 孙福振, 王澳飞, 等. 组合枚举时间间隔对比学习序列推荐[J]. 国防科技大学学报, 2025, 47(4): 170-179.

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  • Received:October 28,2024
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  • Online: July 23,2025
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