VinciZhu/GiffCF

Official implementation of "Graph Signal Diffusion Model for Collaborative Filtering" (SIGIR 2024)

23
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Experimental

This project helps e-commerce managers and content curators improve their recommendation systems. It takes in historical user interactions with items (like purchases or clicks) and generates more accurate recommendations for what a user might like next. The goal is to provide better, more relevant suggestions to users, leading to improved engagement and satisfaction.

No commits in the last 6 months.

Use this if you manage a platform with many users and items, and you want to generate highly accurate, personalized recommendations based on implicit user feedback.

Not ideal if your recommendation system relies heavily on explicit user ratings (like star reviews) rather than just implicit interactions.

e-commerce recommendation-systems user-engagement content-discovery personalization
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 8 / 25
Community 9 / 25

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17

Forks

2

Language

Python

License

Last pushed

May 31, 2024

Commits (30d)

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