TensorRec VS reclist

Compare TensorRec vs reclist and see what are their differences.

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TensorRec reclist
- 1
1,258 449
- 0.2%
0.0 7.8
12 months ago 9 months 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.

reclist

Posts with mentions or reviews of reclist. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-04-29.

What are some alternatives?

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

implicit - Fast Python Collaborative Filtering for Implicit Feedback Datasets

spotlight - Deep recommender models using PyTorch.

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

evalRS-CIKM-2022 - Official Repository for EvalRS @ CIKM 2022: a Rounded Evaluation of Recommender Systems

fastFM - fastFM: A Library for Factorization Machines

LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.

libffm - A Library for Field-aware Factorization Machines

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.

RecBole - A unified, comprehensive and efficient recommendation library

recommenders - Best Practices on Recommendation Systems