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https://github.com/waterlink/likes
Give it a list of people and their likings and it will tell what else could these people like. Ruby gem.
https://github.com/waterlink/likes
Last synced: 14 days ago
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Give it a list of people and their likings and it will tell what else could these people like. Ruby gem.
- Host: GitHub
- URL: https://github.com/waterlink/likes
- Owner: waterlink
- License: mit
- Created: 2015-02-18T16:36:30.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2015-02-23T05:22:01.000Z (almost 10 years ago)
- Last Synced: 2024-04-24T16:10:24.603Z (8 months ago)
- Language: Ruby
- Homepage:
- Size: 242 KB
- Stars: 1
- Watchers: 3
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Likes
[](https://travis-ci.org/waterlink/likes)
Give it a list of people and their likings and it will tell what else could these people like.
This is a ruby gem.
## Installation
Add this line to your application's Gemfile:
```ruby
gem 'likes'
```
And then execute:
$ bundle
Or install it yourself as:
$ gem install likes
## Usage
```ruby
require "likes"
likeset = Likes::Set.new([
Likes::Like.new(person: 1, item: 1),
Likes::Like.new(person: 1, item: 5),
Likes::Like.new(person: 1, item: 4),
Likes::Like.new(person: 1, item: 7),
Likes::Like.new(person: 2, item: 1),
Likes::Like.new(person: 2, item: 3),
Likes::Like.new(person: 2, item: 5),
Likes::Like.new(person: 2, item: 4),
Likes::Like.new(person: 3, item: 3),
Likes::Like.new(person: 3, item: 2),
Likes::Like.new(person: 3, item: 5),
Likes::Like.new(person: 3, item: 4),
Likes::Like.new(person: 3, item: 9),
])
likeset.recommendations_for(1) #=> [3]
likeset.recommendations_for(2) #=> [7, 2, 9]
likeset.recommendations_for(3) #=> [1]
```
### Engines
There are currently 3 different engines:
- `Likes::Engines::BestIntersectionSize` - simplest implementation, calculates liking intersection size for current person with all other people. Chooses maximum intersection size to generate recommendations. Worst complexity: `O(NK) * O(log N + log P)` where `N` - distinct people count, `P` - distinct items count, `K` - how much likes current person has (in practice not very big number, in theory can be as high as `P`).
- `Likes::Engines::BestRelativeIntersectionSize` - the same as previous, but uses relative intersection size to eliminate 'I-like-everything-in-the-world' noise. `Relative intersection size = intersection size / (union size + intersection size)`. Worst complexity is the same as above.
- `Likes::Engines::FastJaccardSimilarity` - much faster implementation. Uses relative intersection size estimation to find people to make a base for recommendations. Uses `minhash` algorithm to calculate set signature for each person, which makes initial dataset much smaller. Allows to reuse for different people without re-calculating set signatures, but currently not implemented. Worst complexity: `D * O(P + N) * O(log N + log P)` where `D` - depth of implementation (count of different hash functions used), currently `D = 200`.
By default `FastJaccardSimilarity` is used. If you want to use different engine, then you can do this:
```ruby
Likes::Set.new(like_list, Likes::Engines::BestRelativeIntersectionSize)
```
#### Implementing your own engine
If you want to implement your own engine, you should look at this protocol: [lib/likes/engines/protocol.rb](lib/likes/engines/protocol.rb)
## Contributing
1. Fork it ( https://github.com/waterlink/likes/fork )
2. Create your feature branch (`git checkout -b my-new-feature`)
3. Commit your changes (`git commit -am 'Add some feature'`)
4. Push to the branch (`git push origin my-new-feature`)
5. Create a new Pull Request