hexiangnan/neural_collaborative_filtering
Neural network implementation for collaborative filtering and recommender systems based on the WWW'17 paper.
Not currently ranked — collecting fresh signals.
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This repository provides implementations of three neural collaborative filtering models: Generalized Matrix Factorization (GMF), Multi-Layer Perceptron (MLP), and Neural Matrix Factorization (NeuMF). The models use deep learning to learn user-item interaction patterns from implicit feedback data. Built with Keras and Theano, the implementation optimizes models using log loss with negative sampling for ranking tasks.
Frequently asked
- What is hexiangnan/neural_collaborative_filtering?
- Neural network implementation for collaborative filtering and recommender systems based on the WWW'17 paper.
- Is neural_collaborative_filtering open source?
- Yes — hexiangnan/neural_collaborative_filtering is open source, released under the Apache-2.0 license.
- What language is neural_collaborative_filtering written in?
- hexiangnan/neural_collaborative_filtering is primarily written in Python.
- How popular is neural_collaborative_filtering?
- hexiangnan/neural_collaborative_filtering has 1.9k stars on GitHub.
- Where can I find neural_collaborative_filtering?
- hexiangnan/neural_collaborative_filtering is on GitHub at https://github.com/hexiangnan/neural_collaborative_filtering.