针对推荐系统中冷门物品推荐不准的问题，提出自适应增强方法提升长尾项目表现。
Frequency-aware Adaptive Contrastive Learning for Sequential Recommendation
- 根据物品频率动态调整数据扰动强度，保护稀有项目信息
- 通过重加权机制放大低频用户行为序列的训练影响，最高提升3.8%准确率
- 特别适合真实场景中长尾物品多、用户行为稀疏的推荐任务
本文重新审视了对比学习在序列推荐中数据增强的作用，发现其对低频物品和稀疏用户行为存在固有偏差。为此，提出FACL框架，引入微观层面的自适应扰动以保护罕见物品完整性，以及宏观层面的重加权机制来增强稀疏与低频交互序列的训练影响。在五个公开基准数据集上的实验表明，FACL持续优于现有数据增强与模型增强方法，推荐准确率最高提升3.8%。细粒度分析证实，FACL显著缓解了低频物品与用户的性能下降问题，展现了其强健的意图保持能力及在真实世界长尾推荐场景中的优越适用性。
原文摘要 · Abstract (English)
In this paper, we revisited the role of data augmentation in contrastive learning for sequential recommendation, revealing its inherent bias against low-frequency items and sparse user behaviors. To address this limitation, we proposed FACL, a frequency-aware adaptive contrastive learning framework that introduces micro-level adaptive perturbation to protect the integrity of rare items, as well as macro-level reweighting to amplify the influence of sparse and rare-interaction sequences during training. Comprehensive experiments on five public benchmark datasets demonstrated that FACL consistently outperforms state-of-the-art data augmentation and model augmentation-based methods, achieving up to 3.8% improvement in recommendation accuracy. Moreover, fine-grained analyses confirm that FACL significantly alleviates the performance drop on low-frequency items and users, highlighting its robust intent-preserving ability and its superior applicability to real-world, long-tail recommendation scenarios.
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