HKUDS/DiffMM
[ACM MM'2024]"DiffMM: Multi-Modal Diffusion Model for Recommendation"
This project helps e-commerce managers, content curators, and marketing professionals improve their recommendation systems. It takes in user interaction data (like purchases or views) and various item features (images, text descriptions, audio) to generate highly relevant recommendations for individual users. The goal is to provide more accurate suggestions than traditional methods.
No commits in the last 6 months.
Use this if you manage an online platform and want to enhance your product or content recommendations by leveraging a wider range of item information beyond just user interactions.
Not ideal if you only have basic user-item interaction data without rich multimedia features for your items.
Stars
99
Forks
5
Language
Python
License
—
Category
Last pushed
Jul 21, 2024
Commits (30d)
0
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