user683/DRGO
[WWW'25]The official implementation of Distributionally Robust Graph Out-of-Distribution Recommendation via Diffusion Model
This project helps e-commerce platforms, content providers, or social networks recommend items to users, especially when encountering new users or items not seen before. It takes existing user-item interaction data and outputs more accurate recommendations, reducing the 'cold start' problem for new items or users. Data scientists or machine learning engineers working on recommendation systems would use this tool.
No commits in the last 6 months.
Use this if you need a recommendation system that performs well even when dealing with new items or users for whom you have limited historical data.
Not ideal if you are looking for a simple, off-the-shelf recommendation solution without the need for advanced model tuning or robustness to out-of-distribution data.
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Language
Python
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Last pushed
Jun 08, 2025
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