A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms

2025-04-23

AI Summary

This paper investigates how foundation models (e.g., GPT, LLaMA, CLIP) fundamentally reshape recommendation systems, focusing on three directions: feature enhancement, generative recommendation, and agent-based interaction. It identifies core challenges in deep integration—including representation alignment, generation controllability, and interaction rationality—and proposes FM4RecSys, the first three-dimensional paradigm framework for foundation model–enabled recommender systems. The framework critically compares performance trade-offs and applicability boundaries across the three pathways. By unifying multimodal representation learning, generative modeling, and agent coordination mechanisms, the work synthesizes state-of-the-art advances and distills key open problems: cross-modal semantic gaps, inference efficiency bottlenecks, and the lack of rigorous evaluation protocols. Finally, it delivers a practical technology roadmap for next-generation, foundation model–driven recommendation systems, offering both theoretical foundations and actionable implementation guidance.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Multiagent Systems: Other Foundations of Multi Agent Systems
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Recommender systems (RS) have become essential in filtering information and personalizing content for users. RS techniques have traditionally relied on modeling interactions between users and items as well as the features of content using models specific to each task. The emergence of foundation models (FMs), large scale models trained on vast amounts of data such as GPT, LLaMA and CLIP, is reshaping the recommendation paradigm. This survey provides a comprehensive overview of the Foundation Models for Recommender Systems (FM4RecSys), covering their integration in three paradigms: (1) Feature-Based augmentation of representations, (2) Generative recommendation approaches, and (3) Agentic interactive systems. We first review the data foundations of RS, from traditional explicit or implicit feedback to multimodal content sources. We then introduce FMs and their capabilities for representation learning, natural language understanding, and multi-modal reasoning in RS contexts. The core of the survey discusses how FMs enhance RS under different paradigms. Afterward, we examine FM applications in various recommendation tasks. Through an analysis of recent research, we highlight key opportunities that have been realized as well as challenges encountered. Finally, we outline open research directions and technical challenges for next-generation FM4RecSys. This survey not only reviews the state-of-the-art methods but also provides a critical analysis of the trade-offs among the feature-based, the generative, and the agentic paradigms, outlining key open issues and future research directions.

Problem

• How foundation models enhance recommender systems' feature-based representations
• Exploring generative approaches for personalized content recommendation
• Assessing agentic interactive systems powered by foundation models

Innovation

• Feature-Based augmentation of representations
• Generative recommendation approaches
• Agentic interactive systems