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.