recommenders
riscv-newop
| recommenders | riscv-newop | |
|---|---|---|
| 1 | 4 | |
| 1,766 | 15 | |
| 0.9% | - | |
| 4.7 | 0.0 | |
| 18 days ago | over 1 year ago | |
| Python | Python | |
| Apache License 2.0 | BSD 2-clause "Simplified" 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.
recommenders
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How to implement MultipleNegativesRankingLoss in TF?
I have found a similar approach here https://github.com/tensorflow/recommenders/blob/main/tensorflow_recommenders/tasks/retrieval.py
riscv-newop
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Python Command not giving any output!
You appear to have picked one part of a larger package and tried to use it as a plot program. If you look at Histogram.py you will see that it doesn't plot anything. It says:
From what i see, the histogram.py file only declares the class but doesn't execute it. You want to use the __main__.py file which takes an argument as an input and executes the histogram class with it.
What are some alternatives?
rexmex - A general purpose recommender metrics library for fair evaluation.
platform-sifive - SiFive: development platform for PlatformIO
Surprise - A Python scikit for building and analyzing recommender systems
spotlight - Deep recommender models using PyTorch.
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
matrix-factorization - Library for matrix factorization for recommender systems using collaborative filtering
TensorRec - A TensorFlow recommendation algorithm and framework in Python.
RecBole - A unified, comprehensive and efficient recommendation library
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
recommenders - Best Practices on Recommendation Systems
implicit - Fast Python Collaborative Filtering for Implicit Feedback Datasets