用自监督方法提升推荐系统准确率与多样性，无需负样本。
Barlow Twins for Sequential Recommendation
- 基于Barlow Twins思想，用冗余消除机制学习用户行为嵌入。
- 在5个公开数据集上提升准确率，长尾物品覆盖率显著改善。
- 仅需调一个超参数即可平衡精度与多样性，适合实际部署。
序列推荐模型需应对稀疏交互数据、流行度偏差以及准确率与多样性之间的冲突。尽管近期对比自监督学习（SSL）方法提升了准确率，但存在大批次需求、依赖人工数据增强及负采样等问题，后者可能加剧流行度偏差。本文提出BT-SR，一种新颖的非对比自监督框架，将Barlow Twins的冗余消除原理融入基于Transformer的下一物品推荐模型中。BT-SR学习到的嵌入能对齐具有相似短期行为的用户，同时保留长期行为差异，无需负样本或人工扰动。这种结构敏感的对齐使模型更有效识别新兴用户意图，并减轻噪声历史上下文的影响。在五个公开基准上的实验表明，BT-SR持续提升下一物品预测准确率，显著增强长尾物品覆盖率与推荐校准性。关键的是，仅通过调节一个超参数即可控制准确率-多样性权衡，便于根据具体应用场景灵活调整。
原文摘要 · Abstract (English)
Sequential recommendation models must navigate sparse interaction data popularity bias and conflicting objectives like accuracy versus diversity While recent contrastive selfsupervised learning SSL methods offer improved accuracy they come with tradeoffs large batch requirements reliance on handcrafted augmentations and negative sampling that can reinforce popularity bias In this paper we introduce BT-SR a novel noncontrastive SSL framework that integrates the Barlow Twins redundancyreduction principle into a Transformerbased nextitem recommender BTSR learns embeddings that align users with similar shortterm behaviors while preserving longterm distinctionswithout requiring negative sampling or artificial perturbations This structuresensitive alignment allows BT-SR to more effectively recognize emerging user intent and mitigate the influence of noisy historical context Our experiments on five public benchmarks demonstrate that BTSR consistently improves nextitem prediction accuracy and significantly enhances longtail item coverage and recommendation calibration Crucially we show that a single hyperparameter can control the accuracydiversity tradeoff enabling practitioners to adapt recommendations to specific application needs
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