# A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms arXiv: https://arxiv.org/abs/2504.16420 ยท HTML: https://mlsanity.com/paper/2504.16420/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Placed in Agentic recommender systems, one of 16 directions in LLM agents and tool use. - Builds on 207 of its 259 references that are in the map. - First posted on arXiv 23 Apr 2025. ## Placed in - LLM agents and tool use / [Agentic recommender systems](https://mlsanity.com/agents/agentic-recommender-systems/) ## Builds on (most cited first, 30 of 207) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022) - [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022) - [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023) - [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023) - [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021) - [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022) - [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023) - [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021) - [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022) - [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023) - [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020) - [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024) - [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022) - [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020) - [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023) - [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022) - [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023) - [Reformer: The Efficient Transformer](https://mlsanity.com/paper/2001.04451/) (2020) - [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023) - [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023) - [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023) ## Built on by (most cited first, 17 of 17) - [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025) - [Self-Improvements in Modern Agentic Systems: A Survey](https://mlsanity.com/paper/2607.13104/) (2026) - [MemoTime: Memory-Augmented Temporal Knowledge Graph Enhanced Large Language Model Reasoning](https://mlsanity.com/paper/2510.13614/) (2025) - [Skill-R1: Agent Skill Evolution via Reinforcement Learning](https://mlsanity.com/paper/2605.09359/) (2026) - [MemWeaver: Weaving Hybrid Memories for Traceable Long-Horizon Agentic Reasoning](https://mlsanity.com/paper/2601.18204/) (2026) - [Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems](https://mlsanity.com/paper/2607.04433/) (2026) - [Factorized Latent Reasoning for LLM-based Recommendation](https://mlsanity.com/paper/2604.26760/) (2026) - [PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation](https://mlsanity.com/paper/2510.12434/) (2025) - [Seeing and Reflecting: Multimodal Memory-Enhanced Agent Collaboration for Recommendation](https://mlsanity.com/paper/2607.07108/) (2026) - [Beyond Negative Transfer: Disentangled Preference-Guided Diffusion for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2509.00389/) (2025) - [Gaussian Mixture Flow Matching with Domain Alignment for Multi-Domain Sequential Recommendation](https://mlsanity.com/paper/2510.21021/) (2025) - [When and What to Recommend: Joint Modeling of Timing and Content for Active Sequential Recommendation](https://mlsanity.com/paper/2511.18717/) (2025) - [Skill-CMIB: Multimodal Agent Skill for Consistent Action via Conditional Multimodal Information Bottleneck](https://mlsanity.com/paper/2605.08526/) (2026) - [F-GRPO: Factorized Group-Relative Policy Optimization for Unified Candidate Generation and Ranking](https://mlsanity.com/paper/2605.12995/) (2026) - [Controlled Personalization in Legacy Media Online Services: A Case Study in News Recommendation](https://mlsanity.com/paper/2510.09136/) (2025) - [ULTRA:Urdu Language Transformer-based Recommendation Architecture](https://mlsanity.com/paper/2602.11836/) (2026) - [RecToolBench: Benchmarking Recommendation-Specific Tool Orchestration under Fuzzy User Intent](https://mlsanity.com/paper/2609.30717/) (2026) ## Abstract (arXiv) 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.