# Linear Recurrent Units for Sequential Recommendation

arXiv: https://arxiv.org/abs/2310.02367 · HTML: https://mlsanity.com/paper/2310.02367/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2023.

## Built on by (most cited first, 30 of 32)

- [A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)](https://mlsanity.com/paper/2404.00579/) (2024)
- [LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking](https://mlsanity.com/paper/2311.02089/) (2023)
- [An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attention](https://mlsanity.com/paper/2312.10325/) (2023)
- [Federated Recommendation via Hybrid Retrieval Augmented Generation](https://mlsanity.com/paper/2403.04256/) (2024)
- [Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation](https://mlsanity.com/paper/2501.14269/) (2025)
- [Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State-Space Architectures from S4 to Mamba](https://mlsanity.com/paper/2503.18970/) (2025)
- [Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop](https://mlsanity.com/paper/2405.17998/) (2024)
- [IGD: Token Decisiveness Modeling via Information Gain in LLMs for Personalized Recommendation](https://mlsanity.com/paper/2506.13229/) (2025)
- [GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems](https://mlsanity.com/paper/2406.10244/) (2024)
- [Temporal Linear Item-Item Model for Sequential Recommendation](https://mlsanity.com/paper/2412.07382/) (2024)
- [Retrieval Augmented Conversational Recommendation with Reinforcement Learning](https://mlsanity.com/paper/2604.04457/) (2026)
- [FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning](https://mlsanity.com/paper/2402.03481/) (2024)
- [CherryRec: Enhancing News Recommendation Quality via LLM-driven Framework](https://mlsanity.com/paper/2406.12243/) (2024)
- [Who You Are Matters: Bridging Topics and Social Roles via LLM-Enhanced Logical Recommendation](https://mlsanity.com/paper/2505.10940/) (2025)
- [From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation](https://mlsanity.com/paper/2509.23649/) (2025)
- [ELASTIC: Efficient Linear Attention for Sequential Interest Compression](https://mlsanity.com/paper/2408.09380/) (2024)
- [Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders](https://mlsanity.com/paper/2505.23053/) (2025)
- [Improving the Performance of Sequential Recommendation Systems with an Extended Large Language Model](https://mlsanity.com/paper/2507.19990/) (2025)
- [FEDIN: Frequency-Enhanced Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2605.01726/) (2026)
- [Transferable Sequential Recommendation via Vector Quantized Meta Learning](https://mlsanity.com/paper/2411.01785/) (2024)
- [LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking](https://mlsanity.com/paper/2506.07449/) (2025)
- [A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2508.19620/) (2025)
- [TV-Rec: Time-Variant Convolutional Filter for Sequential Recommendation](https://mlsanity.com/paper/2510.25259/) (2025)
- [Rotation-free Online Handwritten Character Recognition Using Linear Recurrent Units](https://mlsanity.com/paper/2602.01533/) (2026)
- [CAMMSR: Category-Guided Attentive Mixture of Experts for Multimodal Sequential Recommendation](https://mlsanity.com/paper/2603.04320/) (2026)
- [Debiasing Sequential Recommendation with Time-aware Inverse Propensity Scoring](https://mlsanity.com/paper/2603.04986/) (2026)
- [Aligning Multimodal Sequential Recommendations via Robust Direct Preference Optimization with Sparse MoE](https://mlsanity.com/paper/2603.29259/) (2026)
- [Fusion and Alignment Enhancement with Large Language Models for Tail-item Sequential Recommendation](https://mlsanity.com/paper/2604.03688/) (2026)
- [TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation](https://mlsanity.com/paper/2604.09439/) (2026)
- [Bridging Textual Profiles and Latent User Embeddings for Personalization](https://mlsanity.com/paper/2605.06981/) (2026)

## Abstract (arXiv)

State-of-the-art sequential recommendation relies heavily on self-attention-based recommender models. Yet such models are computationally expensive and often too slow for real-time recommendation. Furthermore, the self-attention operation is performed at a sequence-level, thereby making low-cost incremental inference challenging. Inspired by recent advances in efficient language modeling, we propose linear recurrent units for sequential recommendation (LRURec). Similar to recurrent neural networks, LRURec offers rapid inference and can achieve incremental inference on sequential inputs. By decomposing the linear recurrence operation and designing recursive parallelization in our framework, LRURec provides the additional benefits of reduced model size and parallelizable training. Moreover, we optimize the architecture of LRURec by implementing a series of modifications to address the lack of non-linearity and improve training dynamics. To validate the effectiveness of our proposed LRURec, we conduct extensive experiments on multiple real-world datasets and compare its performance against state-of-the-art sequential recommenders. Experimental results demonstrate the effectiveness of LRURec, which consistently outperforms baselines by a significant margin. Results also highlight the efficiency of LRURec with our parallelized training paradigm and fast inference on long sequences, showing its potential to further enhance user experience in sequential recommendation.