GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models

ACL ARR 2025 July Submission64 Authors

Abstract: In the past year, Generative Recommendations (GRs) have undergone substantial advancements, especially in leveraging the powerful sequence modeling and reasoning capabilities of Large Language Models (LLMs) to enhance overall recommendation performance. LLM-based GRs are forming a new paradigm that is distinctly different from discriminative recommendations, showing strong potential to replace traditional recommendation systems that are heavily dependent on complex, hand-crafted features. In this paper, we provide a comprehensive survey designed to facilitate further research on LLM-based GRs. Initially, we outline the general preliminaries and application cases of LLM-based GRs. Subsequently, we introduce the main considerations during the industrial applications of GRs. Finally, we explore promising directions for LLM-based GRs. We hope that this survey contributes to the ongoing advancement of the GR domain.

Paper Type: Long

Research Area: Language Modeling

Research Area Keywords: applications

Contribution Types: Surveys

Languages Studied: English

Submission Number: 64