mlsanityData to 14 Oct 2026

Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach

arXiv 2305.07001 ↗First posted 11 May 2023cs.IR cs.CLcited by 168

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Abstract (arXiv)

In the past decades, recommender systems have attracted much attention in both research and industry communities, and a large number of studies have been devoted to developing effective recommendation models. Basically speaking, these models mainly learn the underlying user preference from historical behavior data, and then estimate the user-item matching relationships for recommendations. Inspired by the recent progress on large language models (LLMs), we take a different approach to developing the recommendation models, considering recommendation as instruction following by LLMs. The key idea is that the preferences or needs of a user can be expressed in natural language descriptions (called instructions), so that LLMs can understand and further execute the instruction for fulfilling the recommendation task. Instead of using public APIs of LLMs, we instruction tune an open-source LLM (3B Flan-T5-XL), in order to better adapt LLMs to recommender systems. For this purpose, we first design a general instruction format for describing the preference, intention, task form and context of a user in natural language. Then we manually design 39 instruction templates and automatically generate a large amount of user-personalized instruction data (252K instructions) with varying types of preferences and intentions. To demonstrate the effectiveness of our approach, we instantiate the instruction templates into several widely-studied recommendation (or search) tasks, and conduct extensive experiments on these tasks with real-world datasets. Experiment results show that the proposed approach can outperform several competitive baselines, including the powerful GPT-3.5, on these evaluation tasks. Our approach sheds light on developing more user-friendly recommender systems, in which users can freely communicate with the system and obtain more accurate recommendations via natural language instructions.

Key facts. Cited by 168 papers in the map (data through 14 Oct 2026). Builds on 59 of its 129 references that are in the map. First posted on arXiv 11 May 2023.

Builds on

59 of 129 references.

References · 59

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  5. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  6. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  7. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  8. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
  9. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  10. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  11. 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
  12. 2022Training Compute-Optimal Large Language Modelscited by 3,019
38 more
  1. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
  2. 2023A Survey of Large Language Modelscited by 2,576
  3. 2021Scaling Language Models: Methods, Analysis & Insights from Training Gophercited by 1,202
  4. 2018Self-Attentive Sequential Recommendationcited by 1,184
  5. 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,178
  6. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  7. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
  8. 2015Session-based Recommendations with Recurrent Neural Networkscited by 695
  9. 2020Pre-trained Models for Natural Language Processing: A Surveycited by 505
  10. 2018Personalized Top-N Sequential Recommendation via Convolutional Sequence Embeddingcited by 476
  11. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 402
  12. 2023TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendationcited by 337
  13. 2021Pre-Trained Models: Past, Present and Futurecited by 319
  14. 2020S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximizationcited by 311
  15. 2023Specializing Smaller Language Models towards Multi-Step Reasoningcited by 251
  16. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 242
  17. 2023Is ChatGPT a Good Recommender? A Preliminary Studycited by 193
  18. 2023Uncovering ChatGPT's Capabilities in Recommender Systemscited by 161
  19. 2020Embedding-based Retrieval in Facebook Searchcited by 156
  20. 2022M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systemscited by 152
  21. 2020RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithmscited by 138
  22. 2023Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisitedcited by 124
  23. 2022Towards Universal Sequence Representation Learning for Recommender Systemscited by 121
  24. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  25. 2023Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Predictioncited by 116
  26. 2019Towards Knowledge-Based Recommender Dialog Systemcited by 113
  27. 2022Learning Vector-Quantized Item Representation for Transferable Sequential Recommenderscited by 112
  28. 2022Task-aware Retrieval with Instructionscited by 107
  29. 2023GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretationcited by 103
  30. 2020Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systemscited by 98
  31. 2022Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learningcited by 86
  32. 2019Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddingscited by 66
  33. 2021Embedding-based Product Retrieval in Taobao Searchcited by 51
  34. 2021Zero-Shot Recommender Systemscited by 46
  35. 2023Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender Systemscited by 45
  36. 2022RecBole 2.0: Towards a More Up-to-Date Recommendation Librarycited by 39
  37. 2021Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performancecited by 35
  38. 2020One Person, One Model, One World: Learning Continual User Representation without Forgettingcited by 30

50 most cited of 59 All 59 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 34; 2024: 53; 2025: 41; 2026: 40.

168 papers.

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  1. 2023A Survey on Large Language Models for Recommendationcited by 379
  2. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  3. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 309
  4. 2024A Survey on Knowledge Distillation of Large Language Modelscited by 284
  5. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 274
  6. 2024Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalizationcited by 208
  7. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  8. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  9. 2023UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecastingcited by 144
  10. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 142
  11. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systemscited by 116
  12. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 90
38 more
  1. 2023LLM-Rec: Personalized Recommendation via Prompting Large Language Modelscited by 77
  2. 2023ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendationcited by 77
  3. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 73
  4. 2024Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wiselycited by 66
  5. 2024On Softmax Direct Preference Optimization for Recommendationcited by 64
  6. 2024HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modelingcited by 63
  7. 2023Identifying and Mitigating Vulnerabilities in LLM-Integrated Applicationscited by 54
  8. 2023LlamaRec: Two-Stage Recommendation using Large Language Models for Rankingcited by 53
  9. 2024IDGenRec: LLM-RecSys Alignment with Textual ID Learningcited by 53
  10. 2023Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Modelscited by 48
  11. 2023Exploring Large Language Model for Graph Data Understanding in Online Job Recommendationscited by 45
  12. 2024LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generationcited by 45
  13. 2023Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generationcited by 42
  14. 2023RETA-LLM: A Retrieval-Augmented Large Language Model Toolkitcited by 40
  15. 2023E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendationcited by 39
  16. 2025Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learningcited by 33
  17. 2024Customizing Language Models with Instance-wise LoRA for Sequential Recommendationcited by 32
  18. 2023A Large Language Model Enhanced Conversational Recommender Systemcited by 31
  19. 2024Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challengescited by 30
  20. 2023Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answeringcited by 29
  21. 2024Leveraging LLM Reasoning Enhances Personalized Recommender Systemscited by 28
  22. 2023ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Predictioncited by 27
  23. 2024Rethinking Large Language Model Architectures for Sequential Recommendationscited by 27
  24. 2024Large Language Model Distilling Medication Recommendation Modelcited by 26
  25. 2024Can Small Language Models be Good Reasoners for Sequential Recommendation?cited by 26
  26. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  27. 2024CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendationcited by 24
  28. 2024Large Language Models and Video Games: A Preliminary Scoping Reviewcited by 23
  29. 2024RDRec: Rationale Distillation for LLM-based Recommendationcited by 22
  30. 2024On the Essence and Prospect: An Investigation of Alignment Approaches for Big Modelscited by 21
  31. 2024LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Applicationcited by 21
  32. 2023Evaluating ChatGPT as a Recommender System: A Rigorous Approachcited by 20
  33. 2024ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Frameworkcited by 20
  34. 2024Stealthy Attack on Large Language Model based Recommendationcited by 19
  35. 2024Prospect Personalized Recommendation on Large Language Model-based Agent Platformcited by 19
  36. 2024Aligning Large Language Models for Controllable Recommendationscited by 19
  37. 2025LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendationcited by 19
  38. 2025RecoWorld: Building Simulated Environments for Agentic Recommender Systemscited by 19

50 most cited of 168 All 168 (JSON)