neural_collaborative_filtering
implicit
| neural_collaborative_filtering | implicit | |
|---|---|---|
| 1 | 3 | |
| 1,707 | 3,424 | |
| - | - | |
| 0.0 | 6.2 | |
| over 1 year ago | about 1 month ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
neural_collaborative_filtering
-
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
implicit
- Recommendation system integration
-
Content-based Recommender System with Python
Although CF methods also have some explainability available. CF library https://github.com/benfred/implicit which I used a lot in my past projects, e.g. has the method model.explain available for that.
-
Tensorflow Recommender (TFRS) or Scikit-Surprise?
In that case, you are doing some form of collaborative filtering, though you can also add content-based filtering as additional features later. You can use either implicit or explicit feedback. I would suggest checking this package, and this tutorial. Let me know if you have any other questions.
What are some alternatives?
LT-OCF - LT-OCF: Learnable-Time ODE-based Collaborative Filtering, CIKM'21
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
fastFM - fastFM: A Library for Factorization Machines
TensorRec - A TensorFlow recommendation algorithm and framework in Python.
RecBole - A unified, comprehensive and efficient recommendation library
spotlight - Deep recommender models using PyTorch.
libffm - A Library for Field-aware Factorization Machines
Surprise - A Python scikit for building and analyzing recommender systems
matrix-factorization - Library for matrix factorization for recommender systems using collaborative filtering
recommendation-algorithm - Collaborative filtering recommendation system. Recommendation algorithm using collaborative filtering. Topics: Ranking algorithm, euclidean distance algorithm, slope one algorithm, filtragem colaborativa.
movie-recommender - Movie recommender system based on Non-Negative Matrix Factorization and Singular Value Decomposition, with a Flask web interface