RecSysDatasets
implicit
RecSysDatasets | implicit | |
---|---|---|
1 | 3 | |
708 | 3,427 | |
2.0% | - | |
3.9 | 6.2 | |
6 months ago | about 1 month ago | |
Python | Python | |
- | 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.
RecSysDatasets
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Observe differences in the behavior of recommendation models using RecBole
Now, let's continue to try out RecBole on another data set, the second one being FourSquare NYC. I quote the description from https://github.com/RUCAIBox/RecSysDatasets ↓
implicit
- Recommendation system integration
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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.
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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?
RecBole - A unified, comprehensive and efficient recommendation library
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
recbole-item2vec-model - This is a simple item2vec implementation using gensim for recbole( https://recbole.io )
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.
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.