HKUDS/RecDiff
[CIKM'2024] "RecDiff: Diffusion Model for Social Recommendation"
This project helps e-commerce platforms, social media apps, and content providers give better product, content, or connection recommendations to their users. It takes existing user interaction data and social connections, identifies and removes 'noisy' or unhelpful social ties, and then outputs more accurate and relevant personalized recommendations. Social media managers, product recommendation specialists, and platform growth strategists would find this valuable.
No commits in the last 6 months.
Use this if your social recommendation system struggles with inaccurate suggestions due to irrelevant or misleading social connections among users.
Not ideal if your recommendation system doesn't rely on social connections, or if you need a solution for a purely cold-start recommendation problem without existing social graphs.
Stars
89
Forks
6
Language
Python
License
Apache-2.0
Category
Last pushed
Jun 16, 2025
Commits (30d)
0
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