TensorRec VS recommenders

Compare TensorRec vs recommenders and see what are their differences.

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TensorRec recommenders
0 3
1,191 13,502
- 1.9%
0.0 9.8
4 months ago 2 days ago
Python Python
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

TensorRec

Posts with mentions or reviews of TensorRec. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning TensorRec yet.
Tracking mentions began in Dec 2020.

recommenders

Posts with mentions or reviews of recommenders. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-04-01.
  • This Week in Python
    5 projects | dev.to | 1 Apr 2022
    recommenders – Best Practices on Recommendation Systems
  • Input to SVD, SAR, NMF
    1 project | reddit.com/r/learnmachinelearning | 14 Mar 2022
    I would like to do a benchmarking on the Microsoft models SVD, SAR and NMF (available here: https://github.com/microsoft/recommenders) but with this input data I get a precision and recall close to zero. Any ideas how I can improve this? For SVD and NMF (surprise library) the model wants a rating input that is normally distributed, which it not the case for my binary data where the transactions all have a rating of 1.
  • Opinion on choice of model - Recommender System
    2 projects | reddit.com/r/datascience | 10 Apr 2021
    Then I tried to find some more advanced models and I found this really good list and in there I found the Microsoft one. So it's' where we are now, which a bunch of different models and not a documentation/tutorials out there.

What are some alternatives?

When comparing TensorRec and recommenders you can also consider the following projects:

implicit - Fast Python Collaborative Filtering for Implicit Feedback Datasets

annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

fastFM - fastFM: A Library for Factorization Machines

pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]

RecBole - A unified, comprehensive and efficient recommendation library

spotlight - Deep recommender models using PyTorch.

azure-devops-python-api - Azure DevOps Python API

libffm - A Library for Field-aware Factorization Machines

Google-rank-tracker - SEO: Python script + shell script and cronjob to check ranks on a daily basis

spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python

python-minecraft-clone - Source code for each episode of my Minecraft clone in Python YouTube tutorial series.

Surprise - A Python scikit for building and analyzing recommender systems