jasonshere/FairGAN
FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback
This project helps e-commerce managers and content curators create more equitable recommendation lists for their users. It takes in historical user interaction data, like past purchases or clicks, and outputs a refined ranking algorithm. This algorithm ensures that a wider variety of items get fair visibility while still recommending things users are likely to enjoy.
No commits in the last 6 months.
Use this if you manage an online platform with a recommendation system and are concerned about certain items or categories being unfairly overlooked or under-exposed in user recommendations.
Not ideal if your primary goal is solely to maximize user engagement or sales without considering the broader impact of item exposure fairness.
Stars
15
Forks
8
Language
Python
License
Apache-2.0
Category
Last pushed
Oct 08, 2022
Commits (30d)
0
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