user683/CausalDiffRec
[WWW'25]The official implementation of Graph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendation
This project helps build better recommendation systems by understanding user preferences more robustly, even for unusual or niche items. It takes existing user-item interaction data (like purchases, ratings, or views) and processes it to generate improved item recommendations. This is valuable for data scientists and machine learning engineers who are tasked with optimizing personalized content or product suggestions for their users.
No commits in the last 6 months.
Use this if you are developing recommendation systems and need to improve performance, especially when dealing with new items or users whose preferences are hard to predict with traditional methods.
Not ideal if you are looking for a complete, out-of-the-box recommendation API or solution that doesn't require machine learning expertise to implement.
Stars
18
Forks
4
Language
Python
License
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Category
Last pushed
Mar 29, 2025
Commits (30d)
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