mlsanityData to 14 Oct 2026

The Whole is Better than the Sum: Using Aggregated Demonstrations in In-Context Learning for Sequential Recommendation

arXiv 2403.10135 ↗First posted 15 Mar 2024cs.IR cs.AI cs.CLcited by 4

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Abstract (arXiv)

Large language models (LLMs) have shown excellent performance on various NLP tasks. To use LLMs as strong sequential recommenders, we explore the in-context learning approach to sequential recommendation. We investigate the effects of instruction format, task consistency, demonstration selection, and number of demonstrations. As increasing the number of demonstrations in ICL does not improve accuracy despite using a long prompt, we propose a novel method called LLMSRec-Syn that incorporates multiple demonstration users into one aggregated demonstration. Our experiments on three recommendation datasets show that LLMSRec-Syn outperforms state-of-the-art LLM-based sequential recommendation methods. In some cases, LLMSRec-Syn can perform on par with or even better than supervised learning methods. Our code is publicly available at https://github.com/demoleiwang/LLMSRec_Syn.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 38 of its 48 references that are in the map. First posted on arXiv 15 Mar 2024.

Builds on

38 of 48 references.

References · 38

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
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  5. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
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  7. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,434
  8. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,195
  9. 2018Self-Attentive Sequential Recommendationcited by 1,184
  10. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,045
  11. 2015Image-based Recommendations on Styles and Substitutescited by 706
  12. 2015Session-based Recommendations with Recurrent Neural Networkscited by 695
26 more
  1. 2023Automatic Prompt Optimization with "Gradient Descent" and Beam Searchcited by 653
  2. 2021Learning To Retrieve Prompts for In-Context Learningcited by 648
  3. 2023Is ChatGPT a General-Purpose Natural Language Processing Task Solver?cited by 488
  4. 2018Personalized Top-N Sequential Recommendation via Convolutional Sequence Embeddingcited by 476
  5. 2023Larger language models do in-context learning differentlycited by 394
  6. 2022RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learningcited by 366
  7. 2023TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendationcited by 337
  8. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 309
  9. 2021An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQAcited by 301
  10. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 242
  11. 2023Is ChatGPT a Good Recommender? A Preliminary Studycited by 193
  12. 2022In-context Examples Selection for Machine Translationcited by 172
  13. 2016Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendationcited by 152
  14. 2023What Makes Good Examples for Visual In-Context Learning?cited by 150
  15. 2023Unified Demonstration Retriever for In-Context Learningcited by 142
  16. 2022Text and Patterns: For Effective Chain of Thought, It Takes Two to Tangocited by 128
  17. 2022Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrationscited by 116
  18. 2021Sequential Recommendation with Graph Neural Networkscited by 89
  19. 2023Leveraging Large Language Models for Sequential Recommendationcited by 78
  20. 2023How Many Demonstrations Do You Need for In-context Learning?cited by 52
  21. 2022XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsingcited by 33
  22. 2023LLM4Vis: Explainable Visualization Recommendation using ChatGPTcited by 25
  23. 2023Large Language Model Can Interpret Latent Space of Sequential Recommendercited by 19
  24. 2023Dynamic Demonstrations Controller for In-Context Learningcited by 6
  25. 2021Next-item Recommendations in Short Sessionscited by 4
  26. 2023Multi-factor Sequential Re-ranking with Perception-Aware Diversificationcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Improving LLM-powered Recommendations with Personalized Informationcited by 10
  2. 2025ItemRAG: Item-Based Retrieval-Augmented Generation for LLM-Based Recommendationcited by 2
  3. 2025Open-Set Living Need Prediction with Large Language Modelscited by 0
  4. 2026A Comparative Study of Demonstration Selection for Practical Large Language Models-based Next POI Predictioncited by 0