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<p>Data augmentation mitigates data sparsity in sequential recommendations by generating new yet effective data. Most existing work focuses on a single original sequence with item-level augmentations. It ignores the correlations between different users (sequences) and struggles to produce diverse yet reasonable new data across sequences, leading to limited performance improvements. Also, the item-level operation may destroy the integrity of the preference knowledge contained in the original sequences, resulting in incomplete preference learning. In this work, we propose a novel user correlation guided <Emphasis Type="Underline">c</Emphasis>ross-sequence <Emphasis Type="Underline">m</Emphasis>ixing plug-in for sequential <Emphasis Type="Underline">rec</Emphasis>ommendation (UCMRec). Our core idea is to select the appropriate sequence based on the user correlation graph and perform cross-sequence mixup operations at the representation level to generate high-quality samples. Specifically, we construct a user-user graph based on joint interactions and perform two types of searches to get the candidate users with different correlations. Then, we introduce a mixup operation at the sequence representation level to generate diverse yet reasonable samples across sequences. Furthermore, we propose a topology-aware reweighting module to enable the model to adjust the learning intensity based on preference similarity. Comprehensive experiments demonstrate the superiority of our method.</p>

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Beyond Single Sequence: User Correlation Guided Cross-sequence Mixing for Sequential Recommendation

  • 期刊论文

  • 发表日期：

    2026年4月7日

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  • Jiahui Zhang,
  • Yizhou Dang,
  • Enneng Yang,
  • Jianzhe Zhao,
  • Linying Jiang,
  • Guibing Guo,
  • Xingwei Wang

摘要

Data augmentation mitigates data sparsity in sequential recommendations by generating new yet effective data. Most existing work focuses on a single original sequence with item-level augmentations. It ignores the correlations between different users (sequences) and struggles to produce diverse yet reasonable new data across sequences, leading to limited performance improvements. Also, the item-level operation may destroy the integrity of the preference knowledge contained in the original sequences, resulting in incomplete preference learning. In this work, we propose a novel user correlation guided cross-sequence mixing plug-in for sequential recommendation (UCMRec). Our core idea is to select the appropriate sequence based on the user correlation graph and perform cross-sequence mixup operations at the representation level to generate high-quality samples. Specifically, we construct a user-user graph based on joint interactions and perform two types of searches to get the candidate users with different correlations. Then, we introduce a mixup operation at the sequence representation level to generate diverse yet reasonable samples across sequences. Furthermore, we propose a topology-aware reweighting module to enable the model to adjust the learning intensity based on preference similarity. Comprehensive experiments demonstrate the superiority of our method.