# Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond

arXiv: https://arxiv.org/abs/2410.19744 · HTML: https://mlsanity.com/paper/2410.19744/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning.
- Builds on 142 of its 173 references that are in the map.
- First posted on arXiv 10 Oct 2024.

## Placed in

- LLM reasoning / [LLM reasoning for recommendation](https://mlsanity.com/lr/llm-reasoning-for-recommendation/)

## Builds on (most cited first, 30 of 142)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [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)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://mlsanity.com/paper/2304.14178/) (2023)
- [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://mlsanity.com/paper/2110.07602/) (2021)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [A Survey on Large Language Models for Recommendation](https://mlsanity.com/paper/2305.19860/) (2023)
- [TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation](https://mlsanity.com/paper/2305.00447/) (2023)
- [Large Language Models are Zero-Shot Rankers for Recommender Systems](https://mlsanity.com/paper/2305.08845/) (2023)
- [Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System](https://mlsanity.com/paper/2303.14524/) (2023)
- [Recommender Systems in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2307.02046/) (2023)
- [Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations](https://mlsanity.com/paper/2402.17152/) (2024)
- [LLMRec: Large Language Models with Graph Augmentation for Recommendation](https://mlsanity.com/paper/2311.00423/) (2023)
- [Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach](https://mlsanity.com/paper/2305.07001/) (2023)
- [Uncovering ChatGPT's Capabilities in Recommender Systems](https://mlsanity.com/paper/2305.02182/) (2023)
- [Representation Learning with Large Language Models for Recommendation](https://mlsanity.com/paper/2310.15950/) (2023)
- [How Can Recommender Systems Benefit from Large Language Models: A Survey](https://mlsanity.com/paper/2306.05817/) (2023)
- [Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations](https://mlsanity.com/paper/2308.16505/) (2023)
- [RecMind: Large Language Model Powered Agent For Recommendation](https://mlsanity.com/paper/2308.14296/) (2023)
- [Large Language Models as Zero-Shot Conversational Recommenders](https://mlsanity.com/paper/2308.10053/) (2023)
- [Data-efficient Fine-tuning for LLM-based Recommendation](https://mlsanity.com/paper/2401.17197/) (2024)
- [GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation](https://mlsanity.com/paper/2304.03879/) (2023)
- [How to Index Item IDs for Recommendation Foundation Models](https://mlsanity.com/paper/2305.06569/) (2023)
- [CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation](https://mlsanity.com/paper/2310.19488/) (2023)
- [Leveraging Large Language Models for Sequential Recommendation](https://mlsanity.com/paper/2309.09261/) (2023)
- [LLM-Rec: Personalized Recommendation via Prompting Large Language Models](https://mlsanity.com/paper/2307.15780/) (2023)

## Built on by (most cited first, 26 of 26)

- [A Survey of Personalized Large Language Models: Progress and Future Directions](https://mlsanity.com/paper/2502.11528/) (2025)
- [Implicit Reasoning in Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2509.02350/) (2025)
- [A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval](https://mlsanity.com/paper/2503.05659/) (2025)
- [AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems](https://mlsanity.com/paper/2505.19623/) (2025)
- [SPRec: Self-Play to Debias LLM-based Recommendation](https://mlsanity.com/paper/2412.09243/) (2024)
- [APEX: An Extensible and Dynamism-Aware Simulator for Automated Parallel Execution in LLM Serving](https://mlsanity.com/paper/2411.17651/) (2024)
- [PersonaVLM: Long-Term Personalized Multimodal LLMs](https://mlsanity.com/paper/2604.13074/) (2026)
- [Privacy Risks of LLM-Empowered Recommender Systems: An Inversion Attack Perspective](https://mlsanity.com/paper/2508.03703/) (2025)
- [CTRL-Rec: Controlling Recommender Systems With Natural Language](https://mlsanity.com/paper/2510.12742/) (2025)
- [Diffusion Models in Recommendation Systems: A Survey](https://mlsanity.com/paper/2501.10548/) (2025)
- [Research on Low-Latency Inference and Training Efficiency Optimization for Graph Neural Network and Large Language Model-Based Recommendation Systems](https://mlsanity.com/paper/2507.01035/) (2025)
- [Benchmark Leakage Trap: Can We Trust LLM-based Recommendation?](https://mlsanity.com/paper/2602.13626/) (2026)
- [AdNanny: One Reasoning LLM for All Offline Ads Recommendation Tasks](https://mlsanity.com/paper/2602.01563/) (2026)
- [Learning Variable-Length Tokenization for Generative Recommendation](https://mlsanity.com/paper/2605.17779/) (2026)
- [TransLLM: A Unified Multi-Task Foundation Framework for Urban Transportation via Learnable Prompting](https://mlsanity.com/paper/2508.14782/) (2025)
- [RLRF: Competitive Search Agent Design via Reinforcement Learning from Ranker Feedback](https://mlsanity.com/paper/2510.04096/) (2025)
- [Shielded RecRL: Explanation Generation for Recommender Systems without Ranking Degradation](https://mlsanity.com/paper/2601.03608/) (2025)
- [Learning User Interests via Reasoning and Distillation for Cross-Domain News Recommendation](https://mlsanity.com/paper/2602.15005/) (2026)
- [SafeCRS: Personalized Safety Alignment for LLM-Based Conversational Recommender Systems](https://mlsanity.com/paper/2603.03536/) (2026)
- [Iterative Semantic Reasoning from Individual to Group Interests for Generative Recommendation with LLMs](https://mlsanity.com/paper/2603.13934/) (2026)
- [AgenticRec: A Recommendation-Oriented Agentic Framework with Progressive Tool-Integrated Reasoning Optimization](https://mlsanity.com/paper/2603.21613/) (2026)
- [Pre-trained LLMs Meet Sequential Recommenders: Efficient User-Centric Knowledge Distillation](https://mlsanity.com/paper/2604.21536/) (2026)
- [Rethinking Semantic Collaborative Integration: Why Alignment Is Not Enough](https://mlsanity.com/paper/2604.22195/) (2026)
- [Rethinking Fairness in LLM-Based Recommender Systems: A Survey](https://mlsanity.com/paper/2606.28340/) (2026)
- [rEDMRec: Distilling Large Language Model Reasoning into an Editable Experience Memory for Recommendation](https://mlsanity.com/paper/2608.18952/) (2026)
- [RPCBench: A Benchmark for Proactive Premise Critique in LLM-based Recommendation](https://mlsanity.com/paper/2609.00918/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have not only revolutionized the field of natural language processing (NLP) but also have the potential to bring a paradigm shift in many other fields due to their remarkable abilities of language understanding, as well as impressive generalization capabilities and reasoning skills. As a result, recent studies have actively attempted to harness the power of LLMs to improve recommender systems, and it is imperative to thoroughly review the recent advances and challenges of LLM-based recommender systems. Unlike existing work, this survey does not merely analyze the classifications of LLM-based recommendation systems according to the technical framework of LLMs. Instead, it investigates how LLMs can better serve recommendation tasks from the perspective of the recommender system community, thus enhancing the integration of large language models into the research of recommender system and its practical application. In addition, the long-standing gap between academic research and industrial applications related to recommender systems has not been well discussed, especially in the era of large language models. In this review, we introduce a novel taxonomy that originates from the intrinsic essence of recommendation, delving into the application of large language model-based recommendation systems and their industrial implementation. Specifically, we propose a three-tier structure that more accurately reflects the developmental progression of recommendation systems from research to practical implementation, including representing and understanding, scheming and utilizing, and industrial deployment. Furthermore, we discuss critical challenges and opportunities in this emerging field. A more up-to-date version of the papers is maintained at: https://github.com/jindongli-Ai/Next-Generation-LLM-based-Recommender-Systems-Survey.