Latest Research Papers
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.