Frequency-aware Adaptive Contrastive Learning for Sequential Recommendation

• URL: http://arxiv.org/abs/2601.17057v1
• Date: Thu, 22 Jan 2026 06:51:59 GMT
• Title: Frequency-aware Adaptive Contrastive Learning for Sequential Recommendation
• Authors: Zhikai Wang, Weihua Zhang,
• Abstract summary: FACL is a frequency-aware adaptive contrastive learning framework that introduces micro-level adaptive perturbation to protect the integrity of rare items.<n>Experiments on five public benchmark datasets demonstrated that FACL consistently outperforms state-of-the-art data augmentation and model augmentation-based methods.<n>Fine-grained analyses confirm that FACL significantly alleviates the performance drop on low-frequency items and users.
• Score: 8.384470430192875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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