mlsanityData to 14 Oct 2026

OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment

arXiv 2502.18965 ↗First posted 26 Feb 2025cs.IRcited by 173

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Cited by 23 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Recently, generative retrieval-based recommendation systems have emerged as a promising paradigm. However, most modern recommender systems adopt a retrieve-and-rank strategy, where the generative model functions only as a selector during the retrieval stage. In this paper, we propose OneRec, which replaces the cascaded learning framework with a unified generative model. To the best of our knowledge, this is the first end-to-end generative model that significantly surpasses current complex and well-designed recommender systems in real-world scenarios. Specifically, OneRec includes: 1) an encoder-decoder structure, which encodes the user's historical behavior sequences and gradually decodes the videos that the user may be interested in. We adopt sparse Mixture-of-Experts (MoE) to scale model capacity without proportionally increasing computational FLOPs. 2) a session-wise generation approach. In contrast to traditional next-item prediction, we propose a session-wise generation, which is more elegant and contextually coherent than point-by-point generation that relies on hand-crafted rules to properly combine the generated results. 3) an Iterative Preference Alignment module combined with Direct Preference Optimization (DPO) to enhance the quality of the generated results. Unlike DPO in NLP, a recommendation system typically has only one opportunity to display results for each user's browsing request, making it impossible to obtain positive and negative samples simultaneously. To address this limitation, We design a reward model to simulate user generation and customize the sampling strategy. Extensive experiments have demonstrated that a limited number of DPO samples can align user interest preferences and significantly improve the quality of generated results. We deployed OneRec in the main scene of Kuaishou, achieving a 1.6% increase in watch-time, which is a substantial improvement.

Key facts. Cited by 173 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Feb 2025.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 55; 2026: 118.

173 papers.

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  1. 2025PrefillOnly: An Inference Engine for Prefill-only Workloads in Large Language Model Applicationscited by 21
  2. 2025RecoWorld: Building Simulated Environments for Agentic Recommender Systemscited by 19
  3. 2025OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking Systemcited by 18
  4. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  5. 2025MiniOneRec: An Open-Source Framework for Scaling Generative Recommendationcited by 17
  6. 2025HiD-VAE: Interpretable Generative Recommendation via Hierarchical and Disentangled Semantic IDscited by 12
  7. 2025RecGPT Technical Reportcited by 11
  8. 2025CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendationcited by 11
  9. 2026GR2: Generative Reasoning Re-rankercited by 11
  10. 2025MMQ: Multimodal Mixture-of-Quantization Tokenization for Semantic ID Generation and User Behavioral Adaptationcited by 10
  11. 2025SAIL-Embedding Technical Report: Omni-modal Embedding Foundation Modelcited by 10
  12. 2025LLaDA-Rec: Discrete Diffusion for Parallel Semantic ID Generation in Generative Recommendationcited by 10
38 more
  1. 2025Diffusion Generative Recommendation with Continuous Tokenscited by 9
  2. 2026OneReason Technical Reportcited by 8
  3. 2026AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systemscited by 8
  4. 2026WeMM-Embedding: WeChat Multi-Modal Embedding Technical Reportcited by 8
  5. 2025KuaiLive: A Real-time Interactive Dataset for Live Streaming Recommendationcited by 7
  6. 2025Masked Diffusion for Generative Recommendationcited by 7
  7. 2025Understanding Generative Recommendation with Semantic IDs from a Model-scaling Viewcited by 6
  8. 2025AdaSwitch: Balancing Exploration and Guidance in Knowledge Distillation via Adaptive Switchingcited by 6
  9. 2025NEZHA: A Zero-sacrifice and Hyperspeed Decoding Architecture for Generative Recommendationscited by 6
  10. 2026S2GR: Stepwise Semantic-Guided Reasoning in Latent Space for Generative Recommendationcited by 6
  11. 2026Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Acceleratorscited by 6
  12. 2026OneSearch-V2: The Latent Reasoning Enhanced Self-distillation Generative Search Frameworkcited by 6
  13. 2025IGD: Token Decisiveness Modeling via Information Gain in LLMs for Personalized Recommendationcited by 5
  14. 2025TBGRecall: A Generative Retrieval Model for E-commerce Recommendation Scenarioscited by 5
  15. 2025OneLoc: Geo-Aware Generative Recommender Systems for Local Life Servicecited by 5
  16. 2026LLaTTE: Scaling Laws for Multi-Stage Sequence Modeling in Large-Scale Ads Recommendationcited by 5
  17. 2026CRAB: Codebook Rebalancing for Bias Mitigation in Generative Recommendationcited by 5
  18. 2025A Simple Contrastive Framework Of Item Tokenization For Generative Recommendationcited by 4
  19. 2025GRACE: Generative Recommendation via Journey-Aware Sparse Attention on Chain-of-Thought Tokenizationcited by 4
  20. 2025Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendationcited by 4
  21. 2025DiffGRM: Diffusion-based Generative Recommendation Modelcited by 4
  22. 2026CURP: Codebook-based Continuous User Representation for Personalized Generation with LLMscited by 4
  23. 2026HyTRec: A Hybrid Temporal-Aware Attention Architecture for Long Behavior Sequential Recommendationcited by 4
  24. 2026SafeGEO: Understanding Generative Engine Optimization Risks in Recommendation Agentscited by 4
  25. 2025GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networkscited by 3
  26. 2025GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Modelscited by 3
  27. 2025Pctx: Tokenizing Personalized Context for Generative Recommendationcited by 3
  28. 2025Revisiting scalable sequential recommendation with Multi-Embedding Approach and Mixture-of-Expertscited by 3
  29. 2025Don't Waste It: Guiding Generative Recommenders with Structured Human Priors via Multi-Head Decodingcited by 3
  30. 2025LLM-Aligned Geographic Item Tokenization for Local-Life Recommendationcited by 3
  31. 2026When Text-as-Vision Meets Semantic IDs in Generative Recommendation: An Empirical Studycited by 3
  32. 2026Zenith: Scaling up Ranking Models for Billion-scale Livestreaming Recommendationcited by 3
  33. 2026AgentSelect: Benchmark for Narrative Query-to-Agent Recommendationcited by 3
  34. 2026Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillationcited by 3
  35. 2025Who You Are Matters: Bridging Topics and Social Roles via LLM-Enhanced Logical Recommendationcited by 2
  36. 2025Listwise Preference Alignment Optimization for Tail Item Recommendationcited by 2
  37. 2025DGenCTR: Towards a Universal Generative Paradigm for Click-Through Rate Prediction via Discrete Diffusioncited by 2
  38. 2025Spacetime-GR: A Spacetime-Aware Generative Model for Large Scale Online POI Recommendationcited by 2

50 most cited of 173 All 173 (JSON)

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  1. 2023GEMRec: Towards Generative Model Recommendation
  2. 2025A Survey on Generative Recommendation: Data, Model, and Tasks
  3. 2026GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation
  4. 2024DimeRec: A Unified Framework for Enhanced Sequential Recommendation via Generative Diffusion Models
  5. 2025OneRec-Think: In-Text Reasoning for Generative Recommendation
  6. 2026OneTrans-V2: Unifying Retrieval, Pre-rank, and Fine-rank with One Transformer in Industrial Recommender
  7. 2026EchoRec: Multi-Item Prediction-Empowered Generative Recommendation via Cycle-Consistent Preference Alignment
  8. 2026OxygenREC-v2: Internalizing Discrimination into Generative Recommendation ↗