mlsanityData to 14 Oct 2026

GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models

arXiv 2507.06507 ↗First posted 9 Jul 2025cs.IR cs.AIcited by 3

On the map: LLM reasoning → LLM reasoning for recommendation

Abstract (arXiv)

In the past year, Generative Recommendations (GRs) have undergone substantial advancements, especially in leveraging the powerful sequence modeling and reasoning capabilities of Large Language Models (LLMs) to enhance overall recommendation performance. LLM-based GRs are forming a new paradigm that is distinctly different from discriminative recommendations, showing strong potential to replace traditional recommendation systems heavily dependent on complex hand-crafted features. In this paper, we provide a comprehensive survey aimed at facilitating further research of LLM-based GRs. Initially, we outline the general preliminaries and application cases of LLM-based GRs. Subsequently, we introduce the main considerations when LLM-based GRs are applied in real industrial scenarios. Finally, we explore promising directions for LLM-based GRs. We hope that this survey contributes to the ongoing advancement of the GR domain.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning. Builds on 43 of its 77 references that are in the map. First posted on arXiv 9 Jul 2025.

Builds on

43 of 77 references.

References · 43

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31 more
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  18. 2024Language Representations Can be What Recommenders Need: Findings and Potentialscited by 18
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025xLLM Technical Reportcited by 3
  2. 2025Catalog-Native LLM: Speaking Item-ID Dialect with Less Entanglement for Recommendationcited by 1
  3. 2026Robust Post-Training for Generative Recommenders: Why Exponential Reward-Weighted SFT Outperforms RLHFcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation
  2. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond
  3. 2023How Can Recommender Systems Benefit from Large Language Models: A Survey
  4. 2026GR2: Generative Reasoning Re-ranker
  5. 2025A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
  6. 2025EAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integration
  7. 2025Scaling Down, Serving Fast: Compressing and Deploying Efficient LLMs for Recommendation Systems
  8. 2023Empowering Few-Shot Recommender Systems with Large Language Models -- Enhanced Representations