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

TL;DR

This survey reviews the emerging paradigm of generative search and recommendation driven by large language models, highlighting a unified framework, current developments, challenges, and future research directions.

Contribution

It introduces a unified framework for generative search and recommendation, categorizes existing works within this framework, and discusses their strengths, weaknesses, and future challenges.

Findings

Unified framework for generative search and recommendation

Categorization of existing works within the framework

Identification of open problems and future directions

Abstract

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.