Reinforced Preference Optimization for Reasoning-Augmented Recommendations

• URL: http://arxiv.org/abs/2605.21967v1
• Date: Thu, 21 May 2026 03:56:42 GMT
• Title: Reinforced Preference Optimization for Reasoning-Augmented Recommendations
• Abstract summary: We introduce RPORec, a reinforced preference optimization framework for Recommender systems.<n>RPORec unifies an LLM backbone's reasoning ability with a dedicated recommendation head (Rechead) for precise item retrieval.<n>Extensive experiments on public benchmarks and large-scale online deployments show that RPORec consistently outperforms state-of-the-art LLM-based recommendation methods.
• Score: 34.99578717556569
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender systems are critical for delivering personalized content across digital platforms, and recent advances in Large Language Models (LLMs) offer new opportunities to enhance them with richer world knowledge and explicit reasoning capabilities. With the help of reasoning knowledge, recommendations can better infer users' underlying intents, adapt to evolving preferences, and leverage semantic relationships for improved accuracy and interpretability. However, existing reasoning-based recommendation methods often fail to fully align the LLM's reasoning process with recommendation-specific objectives due to structural disruption during integration and difficulties in translating free-form generation into accurate item predictions. In this paper, we introduce RPORec, a reinforced preference optimization framework that unifies an LLM backbone's reasoning ability with a dedicated recommendation head (Rechead) for precise item retrieval. RPORec comprises two stages: (1) Reasoning-Augmented Recommendation Modeling, where high-quality Chain-of-Thought (CoT) reasoning is generated and used as auxiliary knowledge to guide the Rechead in learning recommendation-specific representations; and (2) Advanced Reasoning Refinement and Alignment, in which the trained Rechead produces verifiable rewards to fine-tune the LLM backbone via reinforcement learning, enhancing reasoning quality, structural consistency, and task relevance. Extensive experiments on public benchmarks and large-scale online deployments show that RPORec consistently outperforms state-of-the-art LLM-based recommendation methods, demonstrating the effectiveness of reasoning-augmented recommendation modeling in real-world systems.

Related papers

• Reasoning to Rank: An End-to-End Solution for Exploiting Large Language Models for Recommendation [44.51582748617213]
  Reasoning to Rank is an end-to-end training framework that internalizes recommendation utility optimization into the learning of step-by-step reasoning in language models.<n>Our framework performs reasoning at the user-item level and employs reinforcement learning for end-to-end training of the language model.
  arXiv  Detail & Related papers  (2026-02-13T02:22:48Z)
• Think before Recommendation: Autonomous Reasoning-enhanced Recommender [25.883091131835172]
  RecZero is a reinforcement learning-based recommendation paradigm that abandons the traditional multi-model and multi-stage distillation approach.<n>The paper explores a hybrid paradigm, RecOne, which combines supervised fine-tuning with RL, initializing the model with cold-start reasoning samples and further optimizing it with RL.
  arXiv  Detail & Related papers  (2025-10-27T07:26:32Z)
• Generative Reasoning Recommendation via LLMs [48.45009951684554]
  Large language models (LLMs) face fundamental challenges in functioning as generative reasoning recommendation models (GRRMs)<n>This work explores how to build GRRMs by adapting pre-trained LLMs, which achieves a unified understanding-reasoning-prediction manner for recommendation tasks.<n>We propose GREAM, an end-to-end framework that integrates three components: Collaborative-Semantic Alignment, Reasoning Curriculum Activation, and Sparse-Regularized Group Policy Optimization.
  arXiv  Detail & Related papers  (2025-10-23T17:59:31Z)
• OneRec-Think: In-Text Reasoning for Generative Recommendation [55.53292983432484]
  OneRec-Think is a unified framework that seamlessly integrates dialogue, reasoning, and personalized recommendation.<n>Our proposed "Think-Ahead" architecture enables effective industrial deployment on Kuaishou, achieving a 0.159% gain in APP Stay Time.
  arXiv  Detail & Related papers  (2025-10-13T17:20:13Z)
• Towards Comprehensible Recommendation with Large Language Model Fine-tuning [41.218487308635126]
  We propose a novel Content Understanding from a Collaborative Perspective framework (CURec) for recommendation systems.<n>Curec generates collaborative-aligned content features for more comprehensive recommendations.<n>Experiments on public benchmarks demonstrate the superiority of CURec over existing methods.
  arXiv  Detail & Related papers  (2025-08-11T03:55:31Z)
• R$^2$ec: Towards Large Recommender Models with Reasoning [59.32598867813266]
  We propose R$2$ec, a unified large recommender model with intrinsic reasoning capability.<n>R$2$ec introduces a dual-head architecture that supports both reasoning chain generation and efficient item prediction in a single model.<n>To overcome the lack of annotated reasoning data, we design RecPO, a reinforcement learning framework.
  arXiv  Detail & Related papers  (2025-05-22T17:55:43Z)
• LARES: Latent Reasoning for Sequential Recommendation [96.26996622771593]
  We present LARES, a novel and scalable LAtent REasoning framework for Sequential recommendation.<n>Our proposed approach employs a recurrent architecture that allows flexible expansion of reasoning depth without increasing parameter complexity.<n>Our framework exhibits seamless compatibility with existing advanced models, further improving their recommendation performance.
  arXiv  Detail & Related papers  (2025-05-22T16:22:54Z)
• Improving LLM Interpretability and Performance via Guided Embedding Refinement for Sequential Recommendation [18.13513199455587]
  We propose guided embedding refinement to enhance the embeddings associated with the base recommendation system.<n>We generate guided embeddings that capture domain-relevant semantic information on interpretable attributes.<n>The refined embedding achieves approximately $10%$ to $50%$ gains in Mean Reciprocal Rank (MRR), Recall rate, and Normalized Discounted Cumulative Gain (NDCG)
  arXiv  Detail & Related papers  (2025-04-15T23:03:53Z)
• Reason4Rec: Large Language Models for Recommendation with Deliberative User Preference Alignment [69.11529841118671]
  We propose a new Deliberative Recommendation task, which incorporates explicit reasoning about user preferences as an additional alignment goal.<n>We then introduce the Reasoning-powered Recommender framework for deliberative user preference alignment.
  arXiv  Detail & Related papers  (2025-02-04T07:17:54Z)
• A Survey on Large Language Models for Recommendation [77.91673633328148]
  Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) This survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec)
  arXiv  Detail & Related papers  (2023-05-31T13:51:26Z)

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.