# Frequency-aware Adaptive Contrastive Learning for Sequential Recommendation

arXiv: https://arxiv.org/abs/2601.17057 · HTML: https://mlsanity.com/paper/2601.17057/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2026.

## Built on by (most cited first, 1 of 1)

- [SAGE: Variate-Wise Semantic Augmentation for Vision-Language Time Series Forecasting](https://mlsanity.com/paper/2608.26829/) (2026)

## Abstract (arXiv)

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.