# Frequency Enhanced Hybrid Attention Network for Sequential Recommendation

arXiv: https://arxiv.org/abs/2304.09184 · HTML: https://mlsanity.com/paper/2304.09184/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2023.

## Built on by (most cited first, 30 of 52)

- [Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation](https://mlsanity.com/paper/2408.09698/) (2024)
- [An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attention](https://mlsanity.com/paper/2312.10325/) (2023)
- [Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogs](https://mlsanity.com/paper/2409.18721/) (2024)
- [Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation](https://mlsanity.com/paper/2501.14269/) (2025)
- [Text Matching Improves Sequential Recommendation by Reducing Popularity Biases](https://mlsanity.com/paper/2308.14029/) (2023)
- [SIGMA: Selective Gated Mamba for Sequential Recommendation](https://mlsanity.com/paper/2408.11451/) (2024)
- [RECE: Reduced Cross-Entropy Loss for Large-Catalogue Sequential Recommenders](https://mlsanity.com/paper/2408.02354/) (2024)
- [FineRec:Exploring Fine-grained Sequential Recommendation](https://mlsanity.com/paper/2404.12975/) (2024)
- [Disentangling ID and Modality Effects for Session-based Recommendation](https://mlsanity.com/paper/2404.12969/) (2024)
- [Intention Knowledge Graph Construction for User Intention Relation Modeling](https://mlsanity.com/paper/2412.11500/) (2024)
- [Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation](https://mlsanity.com/paper/2512.01372/) (2025)
- [UniRec: A Dual Enhancement of Uniformity and Frequency in Sequential Recommendations](https://mlsanity.com/paper/2406.18470/) (2024)
- [CSRec: Rethinking Sequential Recommendation from A Causal Perspective](https://mlsanity.com/paper/2409.05872/) (2024)
- [Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation Models](https://mlsanity.com/paper/2410.17276/) (2024)
- [Temporal Linear Item-Item Model for Sequential Recommendation](https://mlsanity.com/paper/2412.07382/) (2024)
- [Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach](https://mlsanity.com/paper/2505.19544/) (2025)
- [RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2312.16563/) (2023)
- [Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation](https://mlsanity.com/paper/2408.11372/) (2024)
- [AgentDR: Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents](https://mlsanity.com/paper/2510.05598/) (2025)
- [Denoising Long- and Short-term Interests for Sequential Recommendation](https://mlsanity.com/paper/2407.14743/) (2024)
- [Measuring Recency Bias In Sequential Recommendation Systems](https://mlsanity.com/paper/2409.09722/) (2024)
- [Dynamic Forgetting and Spatio-Temporal Periodic Interest Modeling for Local-Life Service Recommendation](https://mlsanity.com/paper/2508.02451/) (2025)
- [Exploiting Inter-Session Information with Frequency-enhanced Dual-Path Networks for Sequential Recommendation](https://mlsanity.com/paper/2511.06285/) (2025)
- [Search Intenion Network for Personalized Query Auto-Completion in E-Commerce](https://mlsanity.com/paper/2403.02609/) (2024)
- [Spatial-Temporal Knowledge Distillation for Takeaway Recommendation](https://mlsanity.com/paper/2412.16502/) (2024)
- [Brownian Bridge Diffusion for Sequential Recommendation](https://mlsanity.com/paper/2507.06121/) (2025)
- [UniExtreme: A Universal Foundation Model for Extreme Weather Forecasting](https://mlsanity.com/paper/2508.01426/) (2025)
- [Abacus: Self-Supervised Event Counting-Aligned Distributional Pretraining for Sequential User Modeling](https://mlsanity.com/paper/2512.16581/) (2025)
- [FEDIN: Frequency-Enhanced Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2605.01726/) (2026)
- [ProRL: Effective Reinforcement Learning for Proactive Recommendation via Rectified Policy Gradient Estimation](https://mlsanity.com/paper/2605.28293/) (2026)

## Abstract (arXiv)

The self-attention mechanism, which equips with a strong capability of modeling long-range dependencies, is one of the extensively used techniques in the sequential recommendation field. However, many recent studies represent that current self-attention based models are low-pass filters and are inadequate to capture high-frequency information. Furthermore, since the items in the user behaviors are intertwined with each other, these models are incomplete to distinguish the inherent periodicity obscured in the time domain. In this work, we shift the perspective to the frequency domain, and propose a novel Frequency Enhanced Hybrid Attention Network for Sequential Recommendation, namely FEARec. In this model, we firstly improve the original time domain self-attention in the frequency domain with a ramp structure to make both low-frequency and high-frequency information could be explicitly learned in our approach. Moreover, we additionally design a similar attention mechanism via auto-correlation in the frequency domain to capture the periodic characteristics and fuse the time and frequency level attention in a union model. Finally, both contrastive learning and frequency regularization are utilized to ensure that multiple views are aligned in both the time domain and frequency domain. Extensive experiments conducted on four widely used benchmark datasets demonstrate that the proposed model performs significantly better than the state-of-the-art approaches.