Doc Sim - Efficient algorithm for calculating approximate document similarity

A Ruby implementation of Mining of Massive Datasets's document similarity algorithm. It uses Minhash and Localitiy Sensitive Hashing to efficiently find documents with a high probability of being similar.

Install the gem and add to the application's Gemfile by executing:

$ bundle add doc_sim

If bundler is not being used to manage dependencies, install the gem by executing:

$ gem install doc_sim

1. Shingle your documents using Shingling.shingle (k-shingling). The optimal k value differs based on the type of argument, but 5 is a good first guess.
2. Initialize a Minhash::Minhash.
3. Use the Minhash.signature to get the signature array for each document.
4. Initialize a LocalitySensitiveHashing::LocalitySensitiveHashing with appropriate band and row length, depending on your desired precision and recall.
5. insert each signaturea array with its index into the LSH
6. Take all potentially similar pairs (candiate pairs) from the LSH
7. (Optional) Rigorously calculate the similarity between the candidate pairs. This will need to be done manually.

After checking out the repo, run bin/setup to install dependencies. Then, run rake spec to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment.

To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and the created tag, and push the .gem file to rubygems.org.

Bug reports and pull requests are welcome on GitHub at https://github.com/Forthoney/document_similarity. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the code of conduct.

The gem is available as open source under the terms of the MIT License.

Everyone interacting in the LocalitySensitiveHashing project's codebases, issue trackers, chat rooms and mailing lists is expected to follow the code of conduct.