Skip to content

JuliaRecsys/Surprise.jl

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

49 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Surprise.jl - Wrapper to Surprise Python Package

Installation: at the Julia REPL, Pkg.add("Surprise")

Reporting Issues and Contributing: See CONTRIBUTING.md

Goal

The main aim is to create a framework that facilitates the study of recommender systems in Julia.

Example

julia> using Persa

julia> using DatasetsCF

julia> using Surprise

julia> dataset = DatasetsCF.MovieLens();

julia> model = Surprise.IRSVD(dataset);

julia> Persa.train!(model, dataset)

julia> model[1,1]
Rating: 4.010861934456679 (4)

Algorithms

List of collaborative algorithms:

Algorithm Description
KNNBasic A basic KNN algorithm.
KNNBaseline A basic KNN algorithm but using a baseline factor.
KNNWithMeans A basic KNN algorithm but using user or item mean.
SlopeOne SlopeOne algorithm.
RSVD Regulared SVD. The algorithm is also known as SVD by Funk.
IRSVD Improved Regulared SVD. Extension of RSVD algorithm adding the user and item bias.