mlsanityData to 14 Oct 2026

Zero-Shot Recommendation as Language Modeling

arXiv 2112.04184 ↗First posted 8 Dec 2021cs.CL cs.IRcited by 28

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Abstract (arXiv)

Recommendation is the task of ranking items (e.g. movies or products) according to individual user needs. Current systems rely on collaborative filtering and content-based techniques, which both require structured training data. We propose a framework for recommendation with off-the-shelf pretrained language models (LM) that only used unstructured text corpora as training data. If a user u liked textitMatrix and textitInception, we construct a textual prompt, e.g. textit"Movies like Matrix, Inception, <m>" to estimate the affinity between u and m with LM likelihood. We motivate our idea with a corpus analysis, evaluate several prompt structures, and we compare LM-based recommendation with standard matrix factorization trained on different data regimes. The code for our experiments is publicly available (https://colab.research.google.com/drive/1f1mlZ-FGaLGdo5rPzxf3vemKllbh2esT?usp=sharing).

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). Builds on 10 of its 20 references that are in the map. First posted on arXiv 8 Dec 2021.

Builds on

10 of 20 references.

References · 10

  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. 2019Language Models as Knowledge Bases?cited by 1,936
  4. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
  5. 2016Item2Vec: Neural Item Embedding for Collaborative Filteringcited by 72
  6. 2021Zero-Shot Recommender Systemscited by 46
  7. 2020What does BERT know about books, movies and music? Probing BERT for Conversational Recommendationcited by 36
  8. 2019From Zero-Shot Learning to Cold-Start Recommendationcited by 29
  9. 2018Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviorscited by 27
  10. 2021Zero Shot on the Cold-Start Problem: Model-Agnostic Interest Learning for Recommender Systemscited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 11; 2024: 10; 2025: 5; 2026: 2.

28 papers.

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  1. 2023A Survey on Large Language Models for Recommendationcited by 379
  2. 2024Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendationscited by 188
  3. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 142
  4. 2023GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretationcited by 103
  5. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  6. 2023Zero-Shot Next-Item Recommendation using Large Pretrained Language Modelscited by 53
  7. 2023LlamaRec: Two-Stage Recommendation using Large Language Models for Rankingcited by 53
  8. 2023ChatCounselor: A Large Language Models for Mental Health Supportcited by 44
  9. 2024LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendationscited by 24
  10. 2023Editable User Profiles for Controllable Text Recommendationcited by 16
  11. 2024Federated Recommendation via Hybrid Retrieval Augmented Generationcited by 16
  12. 2023Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendationscited by 15
16 more
  1. 2023Generative Job Recommendations with Large Language Modelcited by 13
  2. 2024Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommender Systemscited by 11
  3. 2023A Survey of Graph Prompting Methods: Techniques, Applications, and Challengescited by 10
  4. 2024A survey on fairness of large language models in e-commerce: progress, application, and challengecited by 10
  5. 2024PAP-REC: Personalized Automatic Prompt for Recommendation Language Modelcited by 4
  6. 2023Pre-trained Neural Recommenders: A Transferable Zero-Shot Framework for Recommendation Systemscited by 3
  7. 2025KERAG_R: Knowledge-Enhanced Retrieval-Augmented Generation for Recommendationcited by 3
  8. 2024Attention Overflow: Language Model Input Blur during Long-Context Missing Items Recommendationcited by 2
  9. 2024Large Language Models as Conversational Movie Recommenders: A User Studycited by 1
  10. 2024Large Language Model Driven Recommendationcited by 0
  11. 2025Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviorscited by 0
  12. 2025SocRipple: A Two-Stage Framework for Cold-Start Video Recommendationscited by 0
  13. 2025Large-scale User Game Lifecycle Representation Learningcited by 0
  14. 2025Towards Efficient Hypergraph and Multi-LLM Agent Recommender Systemscited by 0
  15. 2026GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Modelscited by 0
  16. 2026TEAR: Table Extraction with Attribute Recommendation from Texts via Large Language Modelscited by 0