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

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arxiv 2404.16924 v1 pith:LAVM7JPX submitted 2024-04-25 cs.IR cs.CL

Yongqi Li , Xinyu Lin , Wenjie Wang , Fuli Feng , Liang Pang , Wenjie Li , Liqiang Nie , Xiangnan He
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Tat-Seng Chua
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classification cs.IRcs.CL
keywords generativerecommendationsearchparadigminformationexistingframeworklanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 16 Pith papers

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  5. DeepInterestGR: Mining Deep Multi-Interest Using Multi-Modal LLMs for Generative Recommendation

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  9. EARN: Efficient Inference Acceleration for LLM-based Generative Recommendation by Register Tokens

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    EARN inserts learnable register tokens at both ends of a user prompt, prunes prompt tokens after early layers, and achieves up to 3.79x faster LLM-based recommendation inference with comparable or better accuracy.

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  11. Generating Long Semantic IDs in Parallel for Recommendation

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  13. NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search

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    NExT-Search is a proposed paradigm to collect process-level user feedback in generative AI search through active user debugging and a simulated 'shadow user' agent.

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  15. GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models

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  16. Optimizing Conversational Product Recommendation via Reinforcement Learning

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