mlsanityData to 14 Oct 2026

A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)

arXiv 2404.00579 ↗First posted 31 Mar 2024cs.IR cs.AIcited by 58

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Abstract (arXiv)

Traditional recommender systems (RS) typically use user-item rating histories as their main data source. However, deep generative models now have the capability to model and sample from complex data distributions, including user-item interactions, text, images, and videos, enabling novel recommendation tasks. This comprehensive, multidisciplinary survey connects key advancements in RS using Generative Models (Gen-RecSys), covering: interaction-driven generative models; the use of large language models (LLM) and textual data for natural language recommendation; and the integration of multimodal models for generating and processing images/videos in RS. Our work highlights necessary paradigms for evaluating the impact and harm of Gen-RecSys and identifies open challenges. This survey accompanies a tutorial presented at ACM KDD'24, with supporting materials provided at: https://encr.pw/vDhLq.

Key facts. Cited by 58 papers in the map (data through 14 Oct 2026). Builds on 126 of its 198 references that are in the map. First posted on arXiv 31 Mar 2024.

Builds on

126 of 198 references.

References · 126

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38 more
  1. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,322
  2. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,864
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  15. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,210
  16. 2018Self-Attentive Sequential Recommendationcited by 1,184
  17. 2023ImageBind: One Embedding Space To Bind Them Allcited by 1,175
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  19. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
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  21. 2015Session-based Recommendations with Recurrent Neural Networkscited by 695
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  23. 2018Conditional Neural Processescited by 426
  24. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 402
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50 most cited of 126 All 126 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 13; 2025: 28; 2026: 17.

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  1. 2024Graph Retrieval-Augmented Generation: A Surveycited by 324
  2. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 172
  3. 2025Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representationscited by 35
  4. 2025Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender Systemscited by 32
  5. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  6. 2024A Survey on Diffusion Models for Recommender Systemscited by 20
  7. 2024Retrieval-Augmented Conversational Recommendation with Prompt-based Semi-Structured Natural Language State Trackingcited by 18
  8. 2025ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendationcited by 18
  9. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Surveycited by 18
  10. 2024Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitationcited by 17
  11. 2025GRAPHGPT-O: Synergistic Multimodal Comprehension and Generation on Graphscited by 17
  12. 2024A Survey of Generative Search and Recommendation in the Era of Large Language Modelscited by 15
38 more
  1. 2025The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systemscited by 14
  2. 2024Elaborative Subtopic Query Reformulation for Broad and Indirect Queries in Travel Destination Recommendationcited by 11
  3. 2025Bias Beware: The Impact of Cognitive Biases on LLM-Driven Product Recommendationscited by 11
  4. 2025Toward Holistic Evaluation of Recommender Systems Powered by Generative Modelscited by 11
  5. 2025Stealthy LLM-Driven Data Poisoning Attacks Against Embedding-Based Retrieval-Augmented Recommender Systemscited by 8
  6. 2025Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graphcited by 6
  7. 2025AdaSwitch: Balancing Exploration and Guidance in Knowledge Distillation via Adaptive Switchingcited by 6
  8. 2024A Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender Systemcited by 4
  9. 2025A Simple Contrastive Framework Of Item Tokenization For Generative Recommendationcited by 4
  10. 2026Autonomous Information Seeking: A Roadmap for Agentic Recommender Systemscited by 4
  11. 2025Diffusion Models in Recommendation Systems: A Surveycited by 3
  12. 2025Beyond Unimodal Boundaries: Generative Recommendation with Multimodal Semanticscited by 3
  13. 2025Hoist with His Own Petard: Inducing Guardrails to Facilitate Denial-of-Service Attacks on Retrieval-Augmented Generation of LLMscited by 3
  14. 2025Rethinking Group Recommender Systems in the Era of Generative AI: From One-Shot Recommendations to Agentic Group Decision Supportcited by 3
  15. 2026Beyond Task-Only Matching: Personalized Skill Routing with Counterfactual Evaluationcited by 3
  16. 2024XAI4LLM. Let Machine Learning Models and LLMs Collaborate for Enhanced In-Context Learning in Healthcarecited by 2
  17. 2025MUSS: Multilevel Subset Selection for Relevance and Diversitycited by 2
  18. 2025From Personal to Collective: On the Role of Local and Global Memory in LLM Personalizationcited by 2
  19. 2025xGR: Efficient Generative Recommendation Serving at Scalecited by 2
  20. 2026Grounded Token Initialization for New Vocabulary in LMs for Generative Recommendationcited by 2
  21. 2024Comparative Performance of Collaborative Bandit Algorithms: Effect of Sparsity and Exploration Intensitycited by 1
  22. 2024Large Language Models, and LLM-Based Agents, Should Be Used to Enhance the Digital Public Spherecited by 1
  23. 2025RAG-based User Profiling for Precision Planning in Mixed-precision Over-the-Air Federated Learningcited by 1
  24. 2025Tuning-Free LLM Can Build A Strong Recommender Under Sparse Connectivity And Knowledge Gap Via Extracting Intentcited by 1
  25. 2025Evaluating User Experience in Conversational Recommender Systems: A Systematic Review Across Classical and LLM-Powered Approachescited by 1
  26. 2025M-LLM3REC: A Motivation-Aware User-Item Interaction Framework for Enhancing Recommendation Accuracy with LLMscited by 1
  27. 2026Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curationcited by 1
  28. 2026An Embarrassingly Simple Graph Heuristic Reveals Shortcut-Solvable Benchmarks for Sequential Recommendationcited by 1
  29. 2026TRACE: Tourism Recommendation with Accountable Citation Evidencecited by 1
  30. 2026Learning Variable-Length Tokenization for Generative Recommendationcited by 1
  31. 2026ChronoID: Infusing Explicit Temporal Signals into Semantic IDs for Generative Recommendationcited by 1
  32. 2026Eval4DiRec: A Unified and Systematic Evaluation Framework for Diffusion-based Recommender Systemscited by 1
  33. 2024EventChat: Implementation and user-centric evaluation of a large language model-driven conversational recommender system for exploring leisure events in an SME contextcited by 0
  34. 2025Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendationscited by 0
  35. 2025Understanding User Preferences for Interaction Styles in Conversational Recommender Systems: The Predictive Role of System Qualities, User Experience, and Traitscited by 0
  36. 2025Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modelingcited by 0
  37. 2025Learning Decomposed Contextual Token Representations from Pretrained and Collaborative Signals for Generative Recommendationcited by 0
  38. 2025Controlled Personalization in Legacy Media Online Services: A Case Study in News Recommendationcited by 0

50 most cited of 58 All 58 (JSON)