neural_collaborative_filtering
LT-OCF
| neural_collaborative_filtering | LT-OCF | |
|---|---|---|
| 1 | 1 | |
| 1,707 | 20 | |
| - | - | |
| 0.0 | 10.0 | |
| over 1 year ago | over 1 year ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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neural_collaborative_filtering
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How can I load a dataset of sparse vectors (one-hot encoding) efficiently (or lazily) while fitting to prevent overloading GPU's VRAM.
Code for https://arxiv.org/abs/1708.05031 found: https://github.com/hexiangnan/neural_collaborative_filtering
LT-OCF
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[R] Blurring-Sharpening Process Models for Collaborative Filtering (TLDR: graph filtering-based methods + inspired by SGMs = SOTA models for recommender systems)
We have more exciting works on recommender systems; please check our LT-OCF (code) and HMLET (code)!
What are some alternatives?
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.
BSPM - Blurring-Sharpening Process Models for Collaborative Filtering, SIGIR'23
movie-recommender - Movie recommender system based on Non-Negative Matrix Factorization and Singular Value Decomposition, with a Flask web interface
implicit - Fast Python Collaborative Filtering for Implicit Feedback Datasets
HMLET - Linear, or Non-Linear, That is the Question!, WSDM'22
matrix-factorization - Library for matrix factorization for recommender systems using collaborative filtering