# OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems

arXiv: https://arxiv.org/abs/2306.11134 · HTML: https://mlsanity.com/paper/2306.11134/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2023.

## Built on by (most cited first, 19 of 19)

- [Language is All a Graph Needs](https://mlsanity.com/paper/2308.07134/) (2023)
- [Large Language Models for Generative Recommendation: A Survey and Visionary Discussions](https://mlsanity.com/paper/2309.01157/) (2023)
- [E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation](https://mlsanity.com/paper/2312.02443/) (2023)
- [Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations](https://mlsanity.com/paper/2503.02453/) (2025)
- [Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuning](https://mlsanity.com/paper/2505.16270/) (2025)
- [LightLM: A Lightweight Deep and Narrow Language Model for Generative Recommendation](https://mlsanity.com/paper/2310.17488/) (2023)
- [STAR: A Simple Training-free Approach for Recommendations using Large Language Models](https://mlsanity.com/paper/2410.16458/) (2024)
- [Retrieval-Augmented Purifier for Robust LLM-Empowered Recommendation](https://mlsanity.com/paper/2504.02458/) (2025)
- [Survey of Computerized Adaptive Testing: A Machine Learning Perspective](https://mlsanity.com/paper/2404.00712/) (2024)
- [Deploying Foundation Model Powered Agent Services: A Survey](https://mlsanity.com/paper/2412.13437/) (2024)
- [Can LLMs Outshine Conventional Recommenders? A Comparative Evaluation](https://mlsanity.com/paper/2503.05493/) (2025)
- [Do LLMs Benefit from User and Item Embeddings in Recommendation Tasks?](https://mlsanity.com/paper/2601.04690/) (2026)
- [A Unified Language Model for Large Scale Search, Recommendation, and Reasoning](https://mlsanity.com/paper/2603.17533/) (2026)
- [From Interests to Insights: An LLM Approach to Course Recommendations Using Natural Language Queries](https://mlsanity.com/paper/2412.19312/) (2024)
- [Pivoting Retail Supply Chain with Deep Generative Techniques: Taxonomy, Survey and Insights](https://mlsanity.com/paper/2403.00861/) (2024)
- [Content Moderation in TV Search: Balancing Policy Compliance, Relevance, and User Experience](https://mlsanity.com/paper/2505.17207/) (2025)
- [Compositions of Variant Experts for Integrating Short-Term and Long-Term Preferences](https://mlsanity.com/paper/2506.23170/) (2025)
- [SRBench: A Comprehensive Benchmark for Sequential Recommendation with Large Language Models](https://mlsanity.com/paper/2604.09553/) (2026)
- [When Machines Speak: A Unified Generative Framework for Integrating Machine-Native Symbols into Pretrained Large Language Models](https://mlsanity.com/paper/2608.19529/) (2026)

## Abstract (arXiv)

In recent years, the integration of Large Language Models (LLMs) into recommender systems has garnered interest among both practitioners and researchers. Despite this interest, the field is still emerging, and the lack of open-source R&D platforms may impede the exploration of LLM-based recommendations. This paper introduces OpenP5, an open-source platform designed as a resource to facilitate the development, training, and evaluation of LLM-based generative recommender systems for research purposes. The platform is implemented using encoder-decoder LLMs (e.g., T5) and decoder-only LLMs (e.g., Llama-2) across 10 widely recognized public datasets, catering to two fundamental recommendation tasks: sequential and straightforward recommendations. Recognizing the crucial role of item IDs in LLM-based recommendations, we have also incorporated three item indexing methods within the OpenP5 platform: random indexing, sequential indexing and collaborative indexing. Built on the Transformers library, the platform facilitates easy customization of LLM-based recommendations for users. OpenP5 boasts a range of features including extensible data processing, task-centric optimization, comprehensive datasets and checkpoints, efficient acceleration, and standardized evaluations, making it a valuable tool for the implementation and evaluation of LLM-based recommender systems. The open-source code and pre-trained checkpoints for the OpenP5 library are publicly available at https://github.com/agiresearch/OpenP5.