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.

该研究综述了新兴的信息系统中增强搜索和推荐领域中的生成式搜索和推荐模型，并从一个统一的角度总结了该领域的发展，提出了独特挑战、未解问题和未来发展方向，展望下一个信息需求模式。