# One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems

arXiv: https://arxiv.org/abs/2310.14304 · HTML: https://mlsanity.com/paper/2310.14304/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2023.

## Built on by (most cited first, 17 of 17)

- [Graph Machine Learning in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2404.14928/) (2024)
- [Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2406.03085/) (2024)
- [A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms](https://mlsanity.com/paper/2504.16420/) (2025)
- [Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review](https://mlsanity.com/paper/2402.18590/) (2024)
- [LLM-Enhanced Multimodal Fusion for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2506.17966/) (2025)
- [MMQ: Multimodal Mixture-of-Quantization Tokenization for Semantic ID Generation and User Behavioral Adaptation](https://mlsanity.com/paper/2508.15281/) (2025)
- [Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using LLM Optimizers](https://mlsanity.com/paper/2406.05255/) (2024)
- [Are Longer Prompts Always Better? Prompt Selection in Large Language Models for Recommendation Systems](https://mlsanity.com/paper/2412.14454/) (2024)
- [Large Language Model Empowered Recommendation Meets All-domain Continual Pre-Training](https://mlsanity.com/paper/2504.08949/) (2025)
- [Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders](https://mlsanity.com/paper/2505.23053/) (2025)
- [Sharpness-aware Model Merging with Salience Recovery for LLM-based Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2607.25366/) (2026)
- [Tag-Enriched Multi-Attention with Large Language Models for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2510.09224/) (2025)
- [Empowering Contrastive Federated Sequential Recommendation with LLMs](https://mlsanity.com/paper/2602.09306/) (2026)
- [Comparative Analysis of Neural Retriever-Reranker Pipelines for Retrieval-Augmented Generation over Knowledge Graphs in E-commerce Applications](https://mlsanity.com/paper/2602.22219/) (2025)
- [Breaking the Information Silo: Semantic Personas for Cross-Domain Recommendation](https://mlsanity.com/paper/2606.01783/) (2026)
- [Improving Federated Graph Recommendation with Semantic Guidance](https://mlsanity.com/paper/2606.15277/) (2026)
- [TailorMind: Towards Preference-Aligned Multimodal Content Generation](https://mlsanity.com/paper/2606.23643/) (2026)

## Abstract (arXiv)

Sequential recommendation systems aim to predict users' next likely interaction based on their history. However, these systems face data sparsity and cold-start problems. Utilizing data from other domains, known as multi-domain methods, is useful for alleviating these problems. However, traditional multi-domain methods rely on meaningless ID-based item representation, which makes it difficult to align items with similar meanings from different domains, yielding sup-optimal knowledge transfer. This paper introduces LLM-Rec, a framework that utilizes pre-trained large language models (LLMs) for domain-agnostic recommendation. Specifically, we mix user's behaviors from multiple domains and concatenate item titles into a sentence, then use LLMs for generating user and item representations. By mixing behaviors across different domains, we can exploit the knowledge encoded in LLMs to bridge the semantic across over multi-domain behaviors, thus obtaining semantically rich representations and improving performance in all domains. Furthermore, we explore the underlying reasons why LLMs are effective and investigate whether LLMs can understand the semantic correlations as the recommendation model, and if advanced techniques like scaling laws in NLP also work in recommendations. We conduct extensive experiments with LLMs ranging from 40M to 6.7B to answer the above questions and to verify the effectiveness of LLM-Rec in multi-domain recommendation.