RankGR: Rank-Enhanced Generative Retrieval with Listwise Direct Preference Optimization in Recommendation

• URL: http://arxiv.org/abs/2602.08575v1
• Date: Mon, 09 Feb 2026 12:13:43 GMT
• Title: RankGR: Rank-Enhanced Generative Retrieval with Listwise Direct Preference Optimization in Recommendation
• Authors: Kairui Fu, Changfa Wu, Kun Yuan, Binbin Cao, Dunxian Huang, Yuliang Yan, Junjun Zheng, Jianning Zhang, Silu Zhou, Jian Wu, Kun Kuang,
• Abstract summary: We propose RankGR, a Generative Retrieval method that incorporates listwise direct preference optimization for recommendation.<n>In IAP, we incorporate a novel listwise direct preference optimization strategy into GR, thus facilitating a more comprehensive understanding of the hierarchical user preferences.<n>We implement several practical improvements in training and deployment, ultimately achieving a real-time system capable of handling nearly ten thousand requests per second.
• Score: 36.297513746770456
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative retrieval (GR) has emerged as a promising paradigm in recommendation systems by autoregressively decoding identifiers of target items. Despite its potential, current approaches typically rely on the next-token prediction schema, which treats each token of the next interacted items as the sole target. This narrow focus 1) limits their ability to capture the nuanced structure of user preferences, and 2) overlooks the deep interaction between decoded identifiers and user behavior sequences. In response to these challenges, we propose RankGR, a Rank-enhanced Generative Retrieval method that incorporates listwise direct preference optimization for recommendation. RankGR decomposes the retrieval process into two complementary stages: the Initial Assessment Phase (IAP) and the Refined Scoring Phase (RSP). In IAP, we incorporate a novel listwise direct preference optimization strategy into GR, thus facilitating a more comprehensive understanding of the hierarchical user preferences and more effective partial-order modeling. The RSP then refines the top-λ candidates generated by IAP with interactions towards input sequences using a lightweight scoring module, leading to more precise candidate evaluation. Both phases are jointly optimized under a unified GR model, ensuring consistency and efficiency. Additionally, we implement several practical improvements in training and deployment, ultimately achieving a real-time system capable of handling nearly ten thousand requests per second. Extensive offline performance on both research and industrial datasets, as well as the online gains on the "Guess You Like" section of Taobao, validate the effectiveness and scalability of RankGR.

Related papers

• Beyond the Flat Sequence: Hierarchical and Preference-Aware Generative Recommendations [35.58864660038236]
  We propose a novel framework named HPGR (Hierarchical and Preference-aware Generative Recommender)<n>First, a structure-aware pre-training stage employs a session-based Masked Item Modeling objective to learn a hierarchically-informed and semantically rich item representation space.<n>Second, a preference-aware fine-tuning stage leverages these powerful representations to implement a Preference-Guided Sparse Attention mechanism.
  arXiv  Detail & Related papers  (2026-03-01T08:15:34Z)
• iGRPO: Self-Feedback-Driven LLM Reasoning [88.83313431248473]
  Large Language Models (LLMs) have shown promise in solving complex mathematical problems, yet they still fall short of producing accurate and consistent solutions.<n>We introduce Iterative Group Relative Policy Optimization (iGRPO), a two-stage extension of GRPO that adds dynamic self-conditioning through model-generated drafts.<n>Under matched rollout budgets, iGRPO consistently outperforms GRPO across base models.
  arXiv  Detail & Related papers  (2026-02-09T18:45:11Z)
• HiGR: Efficient Generative Slate Recommendation via Hierarchical Planning and Multi-Objective Preference Alignment [22.73838860623495]
  HiGR is an efficient generative slate recommendation framework that integrates hierarchical planning with listwise preference alignment.<n> Experiments on our large-scale commercial media platform demonstrate that HiGR delivers consistent improvements in both offline evaluations and online deployment.
  arXiv  Detail & Related papers  (2025-12-31T11:16:24Z)
• Listwise Preference Diffusion Optimization for User Behavior Trajectories Prediction [41.53271688465831]
  We formulate User Behavior Trajectory Prediction (UBTP) as a new task setting that explicitly models long-term user preferences.<n>We introduce Listwise Preference Diffusion Optimization (LPDO), a diffusion-based training framework that directly optimize structured preferences over entire item sequences.<n>To rigorously evaluate multi-step prediction quality, we propose the task-specific metric Sequential Match (SeqMatch), which measures exact trajectory agreement, and adopt Perplexity (PPL), which assesses probabilistic fidelity.
  arXiv  Detail & Related papers  (2025-11-01T12:16:24Z)
• Rank-GRPO: Training LLM-based Conversational Recommender Systems with Reinforcement Learning [70.6126069527741]
  ConvRec-R1 is a two-stage framework for end-to-end training of conversational recommender systems.<n>In Stage 1, we construct a behavioral-cloning dataset with a Remap-Reflect-Adjust pipeline.<n>In Stage 2, we propose Rank-GRPO, a principled extension of group relative policy optimization.
  arXiv  Detail & Related papers  (2025-10-23T02:56:00Z)
• Embed Progressive Implicit Preference in Unified Space for Deep Collaborative Filtering [13.24227546548424]
  Generalized Neural Ordinal Logistic Regression (GNOLR) is proposed to capture the structured progression of user engagement.<n>GNOLR enhances predictive accuracy, captures the progression of user engagement, and simplifies the retrieval process.<n>Experiments on ten real-world datasets show that GNOLR significantly outperforms state-of-the-art methods in efficiency and adaptability.
  arXiv  Detail & Related papers  (2025-05-27T08:43:35Z)
• Killing Two Birds with One Stone: Unifying Retrieval and Ranking with a Single Generative Recommendation Model [71.45491434257106]
  Unified Generative Recommendation Framework (UniGRF) is a novel approach that integrates retrieval and ranking into a single generative model.<n>To enhance inter-stage collaboration, UniGRF introduces a ranking-driven enhancer module.<n>UniGRF significantly outperforms existing models on benchmark datasets.
  arXiv  Detail & Related papers  (2025-04-23T06:43:54Z)
• In-context Ranking Preference Optimization [65.5489745857577]
  We propose an In-context Ranking Preference Optimization (IRPO) framework to optimize large language models (LLMs) based on ranking lists constructed during inference.<n>We show IRPO outperforms standard DPO approaches in ranking performance, highlighting its effectiveness in aligning LLMs with direct in-context ranking preferences.
  arXiv  Detail & Related papers  (2025-04-21T23:06:12Z)
• OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment [9.99840965933561]
  We propose OneRec, which replaces the cascaded learning framework with a unified generative model.<n>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.
  arXiv  Detail & Related papers  (2025-02-26T09:25:10Z)
• Unleash LLMs Potential for Recommendation by Coordinating Twin-Tower Dynamic Semantic Token Generator [60.07198935747619]
  We propose Twin-Tower Dynamic Semantic Recommender (T TDS), the first generative RS which adopts dynamic semantic index paradigm. To be more specific, we for the first time contrive a dynamic knowledge fusion framework which integrates a twin-tower semantic token generator into the LLM-based recommender. The proposed T TDS recommender achieves an average improvement of 19.41% in Hit-Rate and 20.84% in NDCG metric, compared with the leading baseline methods.
  arXiv  Detail & Related papers  (2024-09-14T01:45:04Z)
• Unleashing the Potential of Large Language Models as Prompt Optimizers: Analogical Analysis with Gradient-based Model Optimizers [108.72225067368592]
  We propose a novel perspective to investigate the design of large language models (LLMs)-based prompts.<n>We identify two pivotal factors in model parameter learning: update direction and update method.<n>We develop a capable Gradient-inspired Prompt-based GPO.
  arXiv  Detail & Related papers  (2024-02-27T15:05:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.