mlsanityData to 14 Oct 2026

Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)

arXiv 2203.13366 ↗First posted 24 Mar 2022cs.IR cs.AI cs.CLcited by 402

Not on the map. Why?

Cited by 27 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

For a long time, different recommendation tasks typically require designing task-specific architectures and training objectives. As a result, it is hard to transfer the learned knowledge and representations from one task to another, thus restricting the generalization ability of existing recommendation approaches, e.g., a sequential recommendation model can hardly be applied or transferred to a review generation method. To deal with such issues, considering that language can describe almost anything and language grounding is a powerful medium to represent various problems or tasks, we present a flexible and unified text-to-text paradigm called "Pretrain, Personalized Prompt, and Predict Paradigm" (P5) for recommendation, which unifies various recommendation tasks in a shared framework. In P5, all data such as user-item interactions, user descriptions, item metadata, and user reviews are converted to a common format -- natural language sequences. The rich information from natural language assists P5 to capture deeper semantics for personalization and recommendation. Specifically, P5 learns different tasks with the same language modeling objective during pretraining. Thus, it serves as the foundation model for various downstream recommendation tasks, allows easy integration with other modalities, and enables instruction-based recommendation based on prompts. P5 advances recommender systems from shallow model to deep model to big model, and will revolutionize the technical form of recommender systems towards universal recommendation engine. With adaptive personalized prompt for different users, P5 is able to make predictions in a zero-shot or few-shot manner and largely reduces the necessity for extensive fine-tuning. On several recommendation benchmarks, we conduct experiments to show the effectiveness of P5. We release the source code at https://github.com/jeykigung/P5.

Key facts. Cited by 402 papers in the map (data through 14 Oct 2026). Builds on 49 of its 86 references that are in the map. First posted on arXiv 24 Mar 2022.

Builds on

49 of 86 references.

References · 49

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  5. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  6. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  7. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
  8. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
  9. 2021Learning to Prompt for Vision-Language Modelscited by 2,281
  10. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  11. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,470
  12. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,256
37 more
  1. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,195
  2. 2018Self-Attentive Sequential Recommendationcited by 1,184
  3. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,135
  4. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
  5. 2019How Can We Know What Language Models Know?cited by 959
  6. 2016Wide & Deep Learning for Recommender Systemscited by 747
  7. 2022OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Frameworkcited by 745
  8. 2021GPT Understands, Toocited by 716
  9. 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 676
  10. 2018Personalized Top-N Sequential Recommendation via Convolutional Sequence Embeddingcited by 476
  11. 2021Unifying Vision-and-Language Tasks via Text Generationcited by 412
  12. 2021Learning How to Ask: Querying LMs with Mixtures of Soft Promptscited by 377
  13. 2020S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximizationcited by 311
  14. 2021PPT: Pre-trained Prompt Tuning for Few-shot Learningcited by 232
  15. 2018Explainable Recommendation: A Survey and New Perspectivescited by 210
  16. 2019Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approachescited by 185
  17. 2021ExT5: Towards Extreme Multi-Task Scaling for Transfer Learningcited by 161
  18. 2020A Survey on Conversational Recommender Systemscited by 143
  19. 2020Neural Collaborative Filtering vs. Matrix Factorization Revisitedcited by 123
  20. 2019MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendationcited by 121
  21. 2018Conversational Recommender Systemcited by 115
  22. 2019Reinforcement Knowledge Graph Reasoning for Explainable Recommendationcited by 102
  23. 2019Hierarchical Gating Networks for Sequential Recommendationcited by 101
  24. 2017Joint Deep Modeling of Users and Items Using Reviews for Recommendationcited by 84
  25. 2020The Turking Test: Can Language Models Understand Instructions?cited by 82
  26. 2020Learning from Task Descriptionscited by 78
  27. 2021Cross-Domain Recommendation: Challenges, Progress, and Prospectscited by 74
  28. 2019A Troubling Analysis of Reproducibility and Progress in Recommender Systems Researchcited by 65
  29. 2021SimpleX: A Simple and Strong Baseline for Collaborative Filteringcited by 65
  30. 2021Counterfactual Explainable Recommendationcited by 60
  31. 2021Personalized Transformer for Explainable Recommendationcited by 59
  32. 2017Neural Rating Regression with Abstractive Tips Generation for Recommendationcited by 50
  33. 2020Neural Collaborative Reasoningcited by 37
  34. 2020One Person, One Model, One World: Learning Continual User Representation without Forgettingcited by 30
  35. 2019From Zero-Shot Learning to Cold-Start Recommendationcited by 29
  36. 2021Scaling Law for Recommendation Models: Towards General-purpose User Representationscited by 26
  37. 2021One4all User Representation for Recommender Systems in E-commercecited by 12

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 7; 2023: 65; 2024: 100; 2025: 106; 2026: 124.

402 papers.

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  1. 2023A Survey on Large Language Models for Recommendationcited by 379
  2. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 309
  3. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 242
  4. 2023Language is All a Graph Needscited by 220
  5. 2024Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalizationcited by 208
  6. 2023Recommender Systems with Generative Retrievalcited by 198
  7. 2023OpenAGI: When LLM Meets Domain Expertscited by 197
  8. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  9. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  10. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  11. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 142
  12. 2023RecMind: Large Language Model Powered Agent For Recommendationcited by 129
38 more
  1. 2023Large Language Models as Zero-Shot Conversational Recommenderscited by 127
  2. 2023Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Predictioncited by 116
  3. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systemscited by 116
  4. 2022Learning Vector-Quantized Item Representation for Transferable Sequential Recommenderscited by 112
  5. 2023GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretationcited by 103
  6. 2023How to Index Item IDs for Recommendation Foundation Modelscited by 102
  7. 2024AIOS: LLM Agent Operating Systemcited by 97
  8. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 90
  9. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  10. 2023LLM-Rec: Personalized Recommendation via Prompting Large Language Modelscited by 77
  11. 2023ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendationcited by 77
  12. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 73
  13. 2023Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferencescited by 66
  14. 2024On Softmax Direct Preference Optimization for Recommendationcited by 64
  15. 2023GenRec: Large Language Model for Generative Recommendationcited by 63
  16. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  17. 2025Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendationcited by 57
  18. 2023Zero-Shot Next-Item Recommendation using Large Pretrained Language Modelscited by 53
  19. 2023LlamaRec: Two-Stage Recommendation using Large Language Models for Rankingcited by 53
  20. 2024IDGenRec: LLM-RecSys Alignment with Textual ID Learningcited by 53
  21. 2023VIP5: Towards Multimodal Foundation Models for Recommendationcited by 46
  22. 2024eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Datacited by 46
  23. 2024Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Eracited by 46
  24. 2023Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestioncited by 43
  25. 2023UP5: Unbiased Foundation Model for Fairness-aware Recommendationcited by 38
  26. 2023LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystemcited by 38
  27. 2024TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendationcited by 35
  28. 2025Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representationscited by 35
  29. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  30. 2025Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learningcited by 33
  31. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  32. 2024Customizing Language Models with Instance-wise LoRA for Sequential Recommendationcited by 32
  33. 2023RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agentscited by 30
  34. 2023LLMRec: Benchmarking Large Language Models on Recommendation Taskcited by 30
  35. 2024Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recencycited by 28
  36. 2024Leveraging LLM Reasoning Enhances Personalized Recommender Systemscited by 28
  37. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  38. 2023ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Predictioncited by 27

50 most cited of 402 All 402 (JSON)