Getting Started

Installation

This package is registered in JuliaRegistries.

julia> using Pkg; Pkg.add("Recommendation")

Usage

The package contains a unified DataAccessor module and several non-personalized/personalized recommenders, as well as evaluation metrics such as Recall. All of them can be accessible by loading the package as follows:

using Recommendation

First of all, you need to create a data accessor from a matrix:

using SparseArrays

data = DataAccessor(sparse([1 0 0; 4 5 0]))

or set of events:

n_users, n_items = 5, 10

events = [Event(1, 2, 1), Event(3, 2, 1), Event(2, 6, 4)]

data = DataAccessor(events, n_users, n_items)

where Event() is a composite type which represents a user-item interaction:

type Event
    user::Integer
    item::Integer
    value::Infinite
end

Note that Infinite is a custom type Union{AbstractFloat, Integer} that represents either floating point or integer number.

Next, you can pass the data accessor to an arbitrary recommender as:

recommender = MostPopular(data)

and building a recommendation engine should be easy:

fit!(recommender)

Personalized recommenders sometimes require us to specify the hyperparameters:

help?> Recommendation.MatrixFactorization
  MatrixFactorization(
      data::DataAccessor,
      n_factors::Integer
  )

recommender = MatrixFactorization(data, 2)
fit!(recommender, learning_rate=15e-4, max_iter=100)

Once a recommendation engine has been built successfully, top-2 recommendation for a user 4 is performed as follows:

# for user#4, pick top-2 from all items
recommend(recommender, 4, 2, collect(1:n_items))