AmanPriyanshu/Federated-Recommendation-Neural-Collaborative-Filtering
Federated Neural Collaborative Filtering (FedNCF). Neural Collaborative Filtering utilizes the flexibility, complexity, and non-linearity of Neural Network to build a recommender system. Aim to federate this recommendation system.
This project helps businesses and platforms provide better product or content recommendations to their users, especially when user data is spread across multiple locations or systems. It takes information about user interactions (like movie ratings or purchases) and outputs a personalized list of recommended items for each user. It's designed for anyone managing recommendation systems who needs to improve accuracy while respecting data privacy or distributed data architectures.
No commits in the last 6 months.
Use this if you need to build or enhance a recommendation engine and want to leverage the power of neural networks across various datasets or client groups without centralizing all raw user data.
Not ideal if your recommendation needs are simple, your user data is fully centralized and not subject to privacy constraints, or you prefer rule-based recommendations over machine learning approaches.
Stars
44
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5
Language
Python
License
MIT
Category
Last pushed
Apr 13, 2023
Commits (30d)
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