Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
src
 
 
 
 
 
 
 
 
 
 
 
 

BPR.jl

Implementation of Rendle et. al 2009 Bayesian Personalized Ranking for Matrix Factorization.

Pkg.clone("https://github.com/jsams/BPR.jl.git")
using BPR
# generate some data. The values are unimportant, only zero versus > 0
# item x user matrix
X = sprand(3000, 4000, 0.05)
# by creating an iterator from the data, can re-use it for other runs
biter = BPR.BPR_iter(X)
@time bpr = BPR.bpr(biter, 10, 0.01, 0.01, 0.01, 0.01; tol=0.01, max_iters=10)
# but could also run straight from the matrix
@time bpr = BPR.bpr(X, 10, 0.01, 0.01, 0.01, 0.01; tol=0.01, max_iters=10)

# did it converge
bpr.converged
# what tolerance was achieved
bpr.value
# what was the BPR-OPT criterion in the last run
bpr.bpr_opt
# how well do we predict in a hold out sample
bpr.auc_outsample 
# matrix of predicted rankings from
bpr.W' * bpr.H

To figure out hyperparamters (number of dimensions, regularizations, and learning rate), a handy function, grid_search can help you with that. It takes the data and a vector for each parameter to search over, and constructs the grid of all those points, running the algorithm for each grid point sample_count times. It constructs a new hold out sample for each run. It returns a DataFrame with the convergence properties and run settings as the columns minus the resulting parameterization. It is built to run in parallel, so starting julia with -p# will run in parallel on # separate processes.

griddf = grid_search(X, sample_count=2; max_iters=100)

Up to you to analyze griddf as to whether you need to refine the grid search or select the optimal hyperparamters.

TODO

  • uniform sampling is maybe not quite uniform, read paper to uniform over what.

About

Implementation of Rendle et. al 2009

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages