HKUDS/DiffKG
[WSDM'2024 Oral] "DiffKG: Knowledge Graph Diffusion Model for Recommendation"
This helps e-commerce platforms, content providers, or social media companies improve their product, content, or friend recommendations. By analyzing user interaction data (what users engage with) and detailed product/content information organized as a knowledge graph, it generates more relevant recommendations. The target users are data scientists or machine learning engineers in these organizations responsible for building and optimizing recommendation systems.
134 stars.
Use this if you are developing recommendation systems and have access to both user interaction data and a knowledge graph describing your items (products, movies, etc.).
Not ideal if you don't have detailed item knowledge organized as a knowledge graph or if you are looking for a plug-and-play solution without needing to train a model.
Stars
134
Forks
14
Language
Python
License
—
Category
Last pushed
Nov 27, 2025
Commits (30d)
0
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