2024-04-25
arXiv

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

Yongqi Li , Xinyu Lin , Wenjie Wang , Fuli Feng , Liang Pang
The paper surveys the emerging paradigm of generative search and recommendation driven by large language models, providing a unified framework to categorize and analyze existing works. It highlights unique challenges, open problems, and future directions in this field.
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.
2023-09-03
arXiv

Large Language Models for Generative Recommendation: A Survey and Visionary Discussions

Lei Li , Yongfeng Zhang , Dugang Liu , Li Chen
This survey explores the potential of large language models (LLMs) in revolutionizing recommender systems by simplifying the recommendation process to a single stage, focusing on direct generation of recommendations. It examines the concept, necessity, and implementation of LLM-based generative recommendation for various tasks.
Large language models (LLM) not only have revolutionized the field of natural language processing (NLP) but also have the potential to reshape many other fields, e.g., recommender systems (RS). However, most of the related work treats an LLM as a component of the conventional recommendation pipeline (e.g., as a feature extractor), which may not be able to fully leverage the generative power of LLM. Instead of separating the recommendation process into multiple stages, such as score computation and re-ranking, this process can be simplified to one stage with LLM: directly generating recommendations from the complete pool of items. This survey reviews the progress, methods, and future directions of LLM-based generative recommendation by examining three questions: 1) What generative recommendation is, 2) Why RS should advance to generative recommendation, and 3) How to implement LLM-based generative recommendation for various RS tasks. We hope that this survey can provide the context and guidance needed to explore this interesting and emerging topic.