goRecommend alternatives and similar packages
Based on the "Machine Learning" category.
Alternatively, view goRecommend alternatives based on common mentions on social networks and blogs.
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Gorgonia
Gorgonia is a library that helps facilitate machine learning in Go. -
m2cgen
Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies -
gosseract
Go package for OCR (Optical Character Recognition), by using Tesseract C++ library -
gago
:four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution) -
ocrserver
A simple OCR API server, seriously easy to be deployed by Docker, on Heroku as well -
onnx-go
onnx-go gives the ability to import a pre-trained neural network within Go without being linked to a framework or library. -
shield
Bayesian text classifier with flexible tokenizers and storage backends for Go -
neat
Plug-and-play, parallel Go framework for NeuroEvolution of Augmenting Topologies (NEAT). -
go-featureprocessing
๐ฅ Fast, simple sklearn-like feature processing for Go -
neural-go
A multilayer perceptron network implemented in Go, with training via backpropagation. -
go-cluster
k-modes and k-prototypes clustering algorithms implementation in Go
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README
Go Recommend
Recommendation algorithms (Collaborative Filtering) in Go!
Background
Collaborative Filtering (CF) is oftentimes used for item recommendations for users, and many libraries exist for other languages (popular implementations include Mahout, Prediction.IO, Apache MLLib ALS etc..). As there are very few machine learning packages out there for Go, I decided to put together some model based CF algorithms that I thought were interesting.
Collaborative Filters inside this package. See each folder for examples/specifications
- Alternating Least Squares (more info here ) for both the Implicit and Explicit Case
- Tests now complete
- Use the implicit case for a confidence rating; explicit for predicting ratings
- Simple Bayesian Collaborative Filtering Algorithm, see details here
- Tests complete
- Similarity/Memory-based (using correlation, cosine and jaccard similarity) based CF, which incorporates a nearest neighbor type metric can be found in the CF folder.
- Tests complete
- See README for more details
- Todo: consider approximate nearest neighbors algorithm.
Most of the recommendation algorithms in this package are briefly outlined in this article
Additional
- If you have any questions/comments, please feel free to reach me at tim [dot] kaye [at] lytics [dot] io