mlsanityData to 7 Oct 2026

Generative Recommendation: Towards Next-generation Recommender Paradigm

arXiv 2304.03516 ↗First posted 7 Apr 2023cs.IRcited by 69

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Abstract (arXiv)

Recommender systems typically retrieve items from an item corpus for personalized recommendations. However, such a retrieval-based recommender paradigm faces two limitations: 1) the human-generated items in the corpus might fail to satisfy the users' diverse information needs, and 2) users usually adjust the recommendations via inefficient passive feedback, e.g., clicks. Nowadays, AI-Generated Content (AIGC) has revealed significant success, offering the potential to overcome these limitations: 1) generative AI can produce personalized items to satisfy users' information needs, and 2) the newly emerged large language models significantly reduce the efforts of users to precisely express information needs via natural language instructions. In this light, the boom of AIGC points the way towards the next-generation recommender paradigm with two new objectives: 1) generating personalized content through generative AI, and 2) integrating user instructions to guide content generation. To this end, we propose a novel Generative Recommender paradigm named GeneRec, which adopts an AI generator to personalize content generation and leverages user instructions. Specifically, we pre-process users' instructions and traditional feedback via an instructor to output the generation guidance. Given the guidance, we instantiate the AI generator through an AI editor and an AI creator to repurpose existing items and create new items. Eventually, GeneRec can perform content retrieval, repurposing, and creation to satisfy users' information needs. Besides, to ensure the trustworthiness of the generated items, we emphasize various fidelity checks. Moreover, we provide a roadmap to envision future developments of GeneRec and several domain-specific applications of GeneRec with potential research tasks. Lastly, we study the feasibility of implementing AI editor and AI creator on micro-video generation.

Key facts. Cited by 69 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Apr 2023.

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Citing papers per year: 2023: 16; 2024: 19; 2025: 21; 2026: 13.

69 papers.

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  1. 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 622
  2. 2023A Survey on Large Language Models for Recommendationcited by 378
  3. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 307
  4. 2024A Survey on Knowledge Distillation of Large Language Modelscited by 276
  5. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  6. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 141
  7. 2023Large Language Models as Zero-Shot Conversational Recommenderscited by 126
  8. 2023Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendationcited by 113
  9. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 89
  10. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  11. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  12. 2023Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generationcited by 42
38 more
  1. 2023Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matchingcited by 40
  2. 2023Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Communitycited by 29
  3. 2024MMREC: LLM Based Multi-Modal Recommender Systemcited by 22
  4. 2024A Survey on Diffusion Models for Recommender Systemscited by 20
  5. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Surveycited by 20
  6. 2023Embedding in Recommender Systems: A Surveycited by 19
  7. 2024Prospect Personalized Recommendation on Large Language Model-based Agent Platformcited by 19
  8. 2023User Modeling in the Era of Large Language Models: Current Research and Future Directionscited by 18
  9. 2024Personalized Image Generation with Large Multimodal Modelscited by 14
  10. 2023Large Language Models are Not Stable Recommender Systemscited by 13
  11. 2025HiD-VAE: Interpretable Generative Recommendation via Hierarchical and Disentangled Semantic IDscited by 11
  12. 2023CgT-GAN: CLIP-guided Text GAN for Image Captioningcited by 10
  13. 2024Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendationcited by 10
  14. 2024RA-Rec: An Efficient ID Representation Alignment Framework for LLM-based Recommendationcited by 7
  15. 2024Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using LLM Optimizerscited by 7
  16. 2025Understanding Generative Recommendation with Semantic IDs from a Model-scaling Viewcited by 6
  17. 2026Multi-Agent Video Recommenders: Evolution, Patterns, and Open Challengescited by 6
  18. 2025DLRREC: Denoising Latent Representations via Multi-Modal Knowledge Fusion in Deep Recommender Systemscited by 5
  19. 2023GEMRec: Towards Generative Model Recommendationcited by 4
  20. 2023A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systemscited by 3
  21. 2024Keyword-driven Retrieval-Augmented Large Language Models for Cold-start User Recommendationscited by 3
  22. 2024Survey for Landing Generative AI in Social and E-commerce Recsys -- the Industry Perspectivescited by 3
  23. 2024Break the ID-Language Barrier: An Adaption Framework for LLM-based Sequential Recommendationcited by 3
  24. 2025Diffusion Models in Recommendation Systems: A Surveycited by 3
  25. 2025xLLM Technical Reportcited by 3
  26. 2025Continuous-time Discrete-space Diffusion Model for Recommendationcited by 3
  27. 2025Multi-Aspect Cross-modal Quantization for Generative Recommendationcited by 3
  28. 2026When Text-as-Vision Meets Semantic IDs in Generative Recommendation: An Empirical Studycited by 3
  29. 2026CURP: Codebook-based Continuous User Representation for Personalized Generation with LLMscited by 3
  30. 2026Rethinking Recommendation Paradigms: From Pipelines to Agentic Recommender Systemscited by 3
  31. 2026Factorized Latent Reasoning for LLM-based Recommendationcited by 3
  32. 2024Exploring ChatGPT for Next-generation Information Retrieval: Opportunities and Challengescited by 2
  33. 2024Multi-Layer Ranking with Large Language Models for News Source Recommendationcited by 2
  34. 2024DifFaiRec: Generative Fair Recommender with Conditional Diffusion Modelcited by 2
  35. 2024Enhancing ID-based Recommendation with Large Language Modelscited by 2
  36. 2025FashionM3: Multimodal, Multitask, and Multiround Fashion Assistant based on Unified Vision-Language Modelcited by 2
  37. 2025Ready2Unlearn: A Learning-Time Approach for Preparing Models with Future Unlearning Readinesscited by 2
  38. 2025Sell It Before You Make It: Revolutionizing E-Commerce with Personalized AI-Generated Itemscited by 1

50 most cited of 69 All 69 (JSON)