Data to 14 Oct 2026

Leveraging Large Language Models for Pre-trained Recommender Systems

arXiv 2308.10837 ↗First posted 21 Aug 2023cs.IRcited by 42

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Abstract (arXiv)

Recent advancements in recommendation systems have shifted towards more comprehensive and personalized recommendations by utilizing large language models (LLM). However, effectively integrating LLM's commonsense knowledge and reasoning abilities into recommendation systems remains a challenging problem. In this paper, we propose RecSysLLM, a novel pre-trained recommendation model based on LLMs. RecSysLLM retains LLM reasoning and knowledge while integrating recommendation domain knowledge through unique designs of data, training, and inference. This allows RecSysLLM to leverage LLMs' capabilities for recommendation tasks in an efficient, unified framework. We demonstrate the effectiveness of RecSysLLM on benchmarks and real-world scenarios. RecSysLLM provides a promising approach to developing unified recommendation systems by fully exploiting the power of pre-trained language models.

Key facts. Cited by 42 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Aug 2023.

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Citing papers per year: 2023: 10; 2024: 18; 2025: 10; 2026: 4.

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2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 700
2024Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalizationcited by 208
2024GliDe with a CaPE: A Low-Hassle Method to Accelerate Speculative Decodingcited by 75
2025TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agentscited by 37
2024A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directionscited by 34
2023Intelligent Virtual Assistants with LLM-based Process Automationcited by 26
2024Large Language Model Distilling Medication Recommendation Modelcited by 26
2023Prompt-augmented Temporal Point Process for Streaming Event Sequencecited by 24
2024RDRec: Rationale Distillation for LLM-based Recommendationcited by 22
2024A Causal Explainable Guardrails for Large Language Modelscited by 18
2024Sora Detector: A Unified Hallucination Detection for Large Text-to-Video Modelscited by 17
2026Reasoning over Semantic IDs Enhances Generative Recommendationcited by 13

30 more

2024Tri-Level Navigator: LLM-Empowered Tri-Level Learning for Time Series OOD Generalizationcited by 12
2025CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendationcited by 11
2023Data-Centric Financial Large Language Modelscited by 8
2024Enhancing High-order Interaction Awareness in LLM-based Recommender Modelcited by 8
2024AAD-LLM: Adaptive Anomaly Detection Using Large Language Modelscited by 7
2025From Prompting to Alignment: A Generative Framework for Query Recommendationcited by 7
2026LookBench: A Live and Holistic Open Benchmark for Fashion Image Retrievalcited by 7
2024AstroMLab 2: AstroLLaMA-2-70B Model and Benchmarking Specialised LLMs for Astronomycited by 6
2023Segment Any Events via Weighted Adaptation of Pivotal Tokenscited by 5
2024PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methodscited by 4
2025RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integrationcited by 4
2023Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identificationcited by 2
2025DORAEMON: A Unified Library for Visual Object Modeling and Representation Learning at Scalecited by 2
2026TwiSTAR:Think Fast, Think Slow, Then Act,Generative Recommendation with Adaptive Reasoningcited by 2
2025DP-GPT4MTS: Dual-Prompt Large Language Model for Textual-Numerical Time Series Forecastingcited by 1
2025LLM4Rec: Large Language Models for Multimodal Generative Recommendation with Causal Debiasingcited by 1
2025Enhance Large Language Models as Recommendation Systems with Collaborative Filteringcited by 1
2023Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inferencecited by 0
2023Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adaptercited by 0
2023GMTR: Graph Matching Transformerscited by 0
2023DarkShot: Lighting Dark Images with Low-Compute and High-Qualitycited by 0
2024TRLS: A Time Series Representation Learning Framework via Spectrogram for Medical Signal Processingcited by 0
2024TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networkscited by 0
2024T-Pixel2Mesh: Combining Global and Local Transformer for 3D Mesh Generation from a Single Imagecited by 0
2024Strategic Arms with Side Communication Prevail Over Low-Regret MAB Algorithmscited by 0
2024Towards General Deepfake Detection with Dynamic Curriculumcited by 0
2024Attr-Int: A Simple and Effective Entity Alignment Framework for Heterogeneous Knowledge Graphscited by 0
2025Mask6D: Masked Pose Priors For 6D Object Pose Estimationcited by 0
2025Learning Decomposed Contextual Token Representations from Pretrained and Collaborative Signals for Generative Recommendationcited by 0
2026Iterative Semantic Reasoning from Individual to Group Interests for Generative Recommendation with LLMscited by 0

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