Recommender system enhanced via LLM graph augmentation (WSDM'24 paper)
Top 66.1% on sourcepulse
LLMRec introduces a novel framework for recommendation systems by leveraging Large Language Models (LLMs) to augment interaction graphs. It targets researchers and practitioners in recommendation systems seeking to enhance model performance by incorporating rich textual and multi-modal content. The primary benefit is improved recommendation accuracy through LLM-driven graph enrichment.
How It Works
LLMRec enhances recommendation models by applying three LLM-based graph augmentation strategies: reinforcing user-item interaction edges, enriching item node attributes with LLM-generated text, and creating user profiles from interaction history. This approach intuitively leverages natural language to capture nuanced relationships and user preferences, offering a more comprehensive understanding of the recommendation landscape compared to traditional methods.
Quick Start & Requirements
pip install -r requirements.txt
python ./gpt_ui_aug.py
, python ./gpt_user_profiling.py
, python ./gpt_i_attribute_generate_aug.py
python ./main.py --dataset {netflix, movielens}
Highlighted Details
Maintenance & Community
The project is associated with the University of Hong Kong and Baidu Inc. The repository was last updated in March 2024.
Licensing & Compatibility
The repository does not explicitly state a license. The provided datasets are for research purposes, with a specific request to cite the paper if the 'netflix' dataset is used.
Limitations & Caveats
The LLM augmentation stages may require significant API costs or computational resources if run directly. The provided code for baselines (LATTICE, MMSSL) requires minor modifications for dataset path adjustments.
1 year ago
1 day