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https://github.com/ankane/cmfrec-ruby
Recommendations for Ruby using collective matrix factorization
https://github.com/ankane/cmfrec-ruby
Last synced: about 1 month ago
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Recommendations for Ruby using collective matrix factorization
- Host: GitHub
- URL: https://github.com/ankane/cmfrec-ruby
- Owner: ankane
- License: mit
- Created: 2020-11-28T03:44:04.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2024-07-27T04:33:58.000Z (about 2 months ago)
- Last Synced: 2024-07-27T05:23:53.636Z (about 2 months ago)
- Language: Ruby
- Size: 105 KB
- Stars: 28
- Watchers: 3
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# cmfrec Ruby
:fire: Recommendations for Ruby, powered by [cmfrec](https://github.com/david-cortes/cmfrec)
- Supports side information :tada:
- Works with explicit and implicit feedback
- Uses high-performance matrix factorization
[](https://github.com/ankane/cmfrec-ruby/actions)
## Installation
Add this line to your application’s Gemfile:
```ruby
gem "cmfrec"
```
For Windows, also follow [these instructions](#windows-installation).
## Getting Started
Create a recommender
```ruby
recommender = Cmfrec::Recommender.new
```
If users rate items directly, this is known as explicit feedback. Fit the recommender with:
```ruby
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:
```ruby
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
```ruby
recommender.user_recs(user_id)
```
Get recommendations for a new user
```ruby
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)
```ruby
recommender.user_recs(user_id, count: 3)
```
Get predicted ratings for specific users and items
```ruby
recommender.predict([{user_id: 1, item_id: 2}, {user_id: 2, item_id: 4}])
```
## Side Information
Add side information about users, items, or both
```ruby
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
```ruby
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
```ruby
recommender.new_user_recs([], user_info: {cats: 0, dogs: 2})
```
## Similarity
Add this line to your application’s Gemfile:
```ruby
gem "ngt"
```
Get similar users
```ruby
recommender.similar_users(user_id)
```
Get similar items - “users who liked this item also liked”
```ruby
recommender.similar_items(item_id)
```
## Examples
### MovieLens
Load the data
```ruby
ratings, user_info, item_info = Cmfrec.load_movielens
```
Create a recommender and get predictions
```ruby
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
[Ahoy](https://github.com/ankane/ahoy) is a great source for implicit feedback
```ruby
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
```ruby
recommender = Cmfrec::Recommender.new
recommender.fit(data)
recommender.user_recs(current_user.id)
```
## Options
Specify the number of factors and epochs
```ruby
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.
### Explicit Feedback
Add implicit features
```ruby
Cmfrec::Recommender.new(add_implicit_features: true)
```
Disable bias
```ruby
Cmfrec::Recommender.new(user_bias: false, item_bias: false)
```
## Data
Data can be an array of hashes
```ruby
[{user_id: 1, item_id: 1, rating: 5}, {user_id: 2, item_id: 1, rating: 3}]
```
Or a Rover data frame
```ruby
Rover.read_csv("ratings.csv")
```
## Storing Recommenders
Store the recommender
```ruby
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](https://github.com/ankane/trove). Also, user and item IDs should be integers or strings for this.
Load a recommender
```ruby
json = File.read("recommender.json")
recommender = Cmfrec::Recommender.load_json(json)
```
Alternatively, you can store only the factors and use a library like [Neighbor](https://github.com/ankane/neighbor). See the [examples](https://github.com/ankane/neighbor/tree/master/examples) for Disco, which has a similar API. For explicit feedback, you should [disable the bias](#explicit-feedback) with this approach.
## Reference
Get ids
```ruby
recommender.user_ids
recommender.item_ids
```
Get the global mean
```ruby
recommender.global_mean
```
Get the factors
```ruby
recommender.user_factors
recommender.item_factors
```
Get the bias
```ruby
recommender.user_bias
recommender.item_bias
```
## Windows Installation
On Windows, build the [cmfrec C shared library](https://github.com/david-cortes/cmfrec#instalation) and set:
```ruby
Cmfrec.ffi_lib = "path/to/cmfrec.dll"
```
## History
View the [changelog](https://github.com/ankane/cmfrec-ruby/blob/master/CHANGELOG.md)
## Contributing
Everyone is encouraged to help improve this project. Here are a few ways you can help:
- [Report bugs](https://github.com/ankane/cmfrec-ruby/issues)
- Fix bugs and [submit pull requests](https://github.com/ankane/cmfrec-ruby/pulls)
- Write, clarify, or fix documentation
- Suggest or add new features
To get started with development:
```sh
git clone https://github.com/ankane/cmfrec-ruby.git
cd cmfrec-ruby
bundle install
bundle exec rake vendor:all
bundle exec rake test
```