title: OneRec: Unifying Retrieve and Rank with Generative Recommender and
    Iterative Preference Alignment
summary: 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.
paper_authors._id: 67c6bfdf96b9f5fa18c517db
paper_authors.name: Jiaxin Deng
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paper_authors.fullname: Jiaxin Deng
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paper_authors.user: OrpheusBetter
paper_authors._id: 67c6bfdf96b9f5fa18c517dc
paper_authors.name: Shiyao Wang
paper_authors.status: admin_assigned
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paper_authors.fullname: shiyao wang
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paper_authors.user: oneself
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paper_authors.name: Kuo Cai
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paper_authors.fullname: CaiKuo
paper_authors.type: user
paper_authors.user: caikuo
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paper_authors.name: Lejian Ren
paper_authors._id: 67c6bfdf96b9f5fa18c517df
paper_authors.name: Qigen Hu
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paper_authors.fullname: huqigen
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paper_authors.user: willis4hu
paper_authors._id: 67c6bfdf96b9f5fa18c517e0
paper_authors.name: Weifeng Ding
paper_authors.status: admin_assigned
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paper_authors._id: 64aeb9342cda6a37a4781b7d
paper_authors.avatarUrl: /avatars/c1584c10ff0f9871315872245c9934fc.svg
paper_authors.fullname: Weifeng Ding
paper_authors.type: user
paper_authors.user: DingWF
paper_authors._id: 67c6bfdf96b9f5fa18c517e1
paper_authors.name: Qiang Luo
paper_authors._id: 67c6bfdf96b9f5fa18c517e2
paper_authors.name: Guorui Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-04T10:16:52.106Z
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paper_authors.fullname: Guorui Zhou
paper_authors.type: user
paper_authors.user: GuoruiZhou
paper_title: OneRec: Unifying Retrieve and Rank with Generative Recommender and
    Iterative Preference Alignment
paper_summary: 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.
paper_ai_summary: OneRec, an end-to-end generative model with encoder-decoder architecture, sparse Mixture-of-Experts, session-wise generation, and Iterative Preference Alignment using Direct Preference Optimization, significantly improves recommendation quality and user watch-time.
paper_ai_keywords: encoder-decoder sparse Mixture-of-Experts session-wise generation Iterative Preference Alignment Direct Preference Optimization reward model