Latest Research Papers
2024-04-25
arXiv
A Survey of Generative Search and Recommendation in the Era of Large Language Models
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
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.