mlsanityData to 14 Oct 2026

A Survey of Generative Search and Recommendation in the Era of Large Language Models

arXiv 2404.16924 ↗First posted 25 Apr 2024cs.IR cs.CLcited by 15

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Abstract (arXiv)

With the information explosion on the Web, search and recommendation are foundational infrastructures to satisfying users' information needs. As the two sides of the same coin, both revolve around the same core research problem, matching queries with documents or users with items. In the recent few decades, search and recommendation have experienced synchronous technological paradigm shifts, including machine learning-based and deep learning-based paradigms. Recently, the superintelligent generative large language models have sparked a new paradigm in search and recommendation, i.e., generative search (retrieval) and recommendation, which aims to address the matching problem in a generative manner. In this paper, we provide a comprehensive survey of the emerging paradigm in information systems and summarize the developments in generative search and recommendation from a unified perspective. Rather than simply categorizing existing works, we abstract a unified framework for the generative paradigm and break down the existing works into different stages within this framework to highlight the strengths and weaknesses. And then, we distinguish generative search and recommendation with their unique challenges, identify open problems and future directions, and envision the next information-seeking paradigm.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 77 of its 154 references that are in the map. First posted on arXiv 25 Apr 2024.

Builds on

77 of 154 references.

References · 77

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38 more
  1. 2019Neural Graph Collaborative Filteringcited by 661
  2. 2018Personalized Top-N Sequential Recommendation via Convolutional Sequence Embeddingcited by 476
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  15. 2023Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendationcited by 114
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  17. 2022Learning Vector-Quantized Item Representation for Transferable Sequential Recommenderscited by 112
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 7; 2026: 4.

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  1. 2025Open Deep Search: Democratizing Search with Open-source Reasoning Agentscited by 49
  2. 2025Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representationscited by 35
  3. 2025Supervising the search process produces reliable and generalizable information-seeking agentscited by 20
  4. 2024LLM-RankFusion: Mitigating Intrinsic Inconsistency in LLM-based Rankingcited by 16
  5. 2025SSRL: Self-Search Reinforcement Learningcited by 14
  6. 2024Efficient Inference for Large Language Model-based Generative Recommendationcited by 11
  7. 2026Rethinking Soft Compression in Retrieval-Augmented Generation: A Query-Conditioned Selector Perspectivecited by 4
  8. 2024Generative Retrieval with Few-shot Indexingcited by 3
  9. 2025Multimodal Data Storage and Retrieval for Embodied AI: A Surveycited by 2
  10. 2024RaCT: Ranking-aware Chain-of-Thought Optimization for LLMscited by 0
  11. 2025Optimizing Conversational Product Recommendation via Reinforcement Learningcited by 0
  12. 2025How good are LLMs at Retrieving Documents in a Specific Domain?cited by 0
3 more
  1. 2026Answer First, Reason Later: Aligning Search Relevance via Mode-Balanced Reinforcement Learningcited by 0
  2. 2026The Vulnerability of LLM Rankers to Prompt Injection Attackscited by 0
  3. 2026Deep Interest Mining for Intent-Enriched Semantic IDs in Multimodal Generative Recommendationcited by 0