cmfrec Ruby

🔥 Recommendations for Ruby, powered by cmfrec

• Supports side information 🎉
• Works with explicit and implicit feedback
• Uses high-performance matrix factorization

Add this line to your application’s Gemfile:

gem "cmfrec"

For Windows, also follow these instructions.

Create a recommender

recommender = Cmfrec::Recommender.new

If users rate items directly, this is known as explicit feedback. Fit the recommender with:

recommender.fit([
  {user_id: 1, item_id: 1, rating: 5},
  {user_id: 2, item_id: 1, rating: 3}
])

IDs can be integers, strings, or any other data type

If users don’t rate items directly (for instance, they’re purchasing items or reading posts), this is known as implicit feedback. Leave out the rating, or use a value like number of purchases, number of page views, or time spent on page:

recommender.fit([
  {user_id: 1, item_id: 1, value: 1},
  {user_id: 2, item_id: 1, value: 1}
])

Use value instead of rating for implicit feedback

Get recommendations for a user in the training data

recommender.user_recs(user_id)

Get recommendations for a new user

recommender.new_user_recs([
  {item_id: 1, rating: 5},
  {item_id: 2, rating: 3}
])

Use the count option to specify the number of recommendations (default is 5)

recommender.user_recs(user_id, count: 3)

Get predicted ratings for specific users and items

recommender.predict([{user_id: 1, item_id: 2}, {user_id: 2, item_id: 4}])

Add side information about users, items, or both

user_info = [
  {user_id: 1, cats: 1, dogs: 0},
  {user_id: 2, cats: 2, dogs: 1}
]
item_info = [
  {item_id: 1, genre_comedy: 1, genre_drama: 0},
  {item_id: 2, genre_comedy: 0, genre_drama: 1}
]
recommender.fit(ratings, user_info: user_info, item_info: item_info)

Get recommendations for a new user with ratings and side information

ratings = [
  {item_id: 1, rating: 5},
  {item_id: 2, rating: 3}
]
recommender.new_user_recs(ratings, user_info: {cats: 0, dogs: 2})

Get recommendations with only side information

recommender.new_user_recs([], user_info: {cats: 0, dogs: 2})

Add this line to your application’s Gemfile:

gem "ngt"

Get similar users

recommender.similar_users(user_id)

Get similar items - “users who liked this item also liked”

recommender.similar_items(item_id)

Load the data

ratings, user_info, item_info = Cmfrec.load_movielens

Create a recommender and get predictions

recommender = Cmfrec::Recommender.new(factors: 20)
recommender.fit(ratings.first(80000), user_info: user_info, item_info: item_info)
recommender.predict(ratings.last(20000))

Ahoy is a great source for implicit feedback

views = Ahoy::Event.where(name: "Viewed post").group(:user_id).group_prop(:post_id).count

data =
  views.map do |(user_id, post_id), count|
    {
      user_id: user_id,
      item_id: post_id,
      value: count
    }
  end

Create a recommender and get recommended posts for a user

recommender = Cmfrec::Recommender.new
recommender.fit(data)
recommender.user_recs(current_user.id)

Specify the number of factors and epochs

Cmfrec::Recommender.new(factors: 8, epochs: 20)

If recommendations look off, trying changing factors. The default is 8, but 3 could be good for some applications and 300 good for others.

Add implicit features

Cmfrec::Recommender.new(add_implicit_features: true)

Disable bias

Cmfrec::Recommender.new(user_bias: false, item_bias: false)

Data can be an array of hashes

[{user_id: 1, item_id: 1, rating: 5}, {user_id: 2, item_id: 1, rating: 3}]

Or a Rover data frame

Rover.read_csv("ratings.csv")

Store the recommender

json = recommender.to_json
File.write("recommender.json", json)

The serialized recommender includes user activity from the training data (to avoid recommending previously rated items), so be sure to protect it. You can save it to a file, database, or any other storage system, or use a tool like Trove. Also, user and item IDs should be integers or strings for this.

Load a recommender

json = File.read("recommender.json")
recommender = Cmfrec::Recommender.load_json(json)

Alternatively, you can store only the factors and use a library like Neighbor. See the examples for Disco, which has a similar API. For explicit feedback, you should disable the bias with this approach.

Get ids

recommender.user_ids
recommender.item_ids

Get the global mean

recommender.global_mean

Get the factors

recommender.user_factors
recommender.item_factors

Get the bias

recommender.user_bias
recommender.item_bias

On Windows, build the cmfrec C shared library and set:

Cmfrec.ffi_lib = "path/to/cmfrec.dll"

View the changelog

Everyone is encouraged to help improve this project. Here are a few ways you can help:

• Report bugs
• Fix bugs and submit pull requests
• Write, clarify, or fix documentation
• Suggest or add new features

To get started with development:

git clone https://github.com/ankane/cmfrec-ruby.git
cd cmfrec-ruby
bundle install
bundle exec rake vendor:all
bundle exec rake test