Download
RecAI is an open-source research platform developed by Microsoft to explore how large language models can be integrated into modern recommender systems. Traditional recommender systems rely on structured behavioral data such as user interactions and item embeddings, while large language models excel at understanding language and reasoning about user preferences. RecAI aims to bridge these two domains by creating architectures and training methods that allow LLMs to function as intelligent recommendation engines. The project explores several approaches, including fine-tuning language models using user behavior data, building recommender agents, and using LLMs to explain recommendation results. RecAI also investigates how conversational interfaces powered by LLMs can improve the personalization and transparency of recommendation systems.
Features
- Framework for integrating large language models into recommender systems
- Support for fine-tuning LLMs using user behavior and item data
- Recommender AI agents capable of generating personalized suggestions
- Prompt-based knowledge injection for improving recommendation quality
- Tools for evaluating LLM-driven recommendation pipelines
- LLM-based explanation modules for improving transparency of recommendations
Project Samples
