Frequency-aware Adaptive Contrastive Learning for Sequential Recommendation

Zhikai Wang; Weihua Zhang

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arXiv · 2601.17057

Frequency-aware Adaptive Contrastive Learning for Sequential Recommendation

Zhikai Wang·Weihua Zhang

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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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Zhikai Wang, Weihua Zhang. 2026-01-22. Frequency-aware Adaptive Contrastive Learning for Sequential Recommendation. https://arxiv.org/abs/2601.17057

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