You can create "channels" to listen for relevant stories, and you rate the articles that you like or dislike.
Over time, your channel learns about your preferences and delivers customized recommendations based on article characteristics such as author, publication, word-count, keywords, readability metrics, and more.
Utilizing our own in-house machine learning system and content-ranking algorithm, NewsShark does for the news what Pandora does for music: we filter out the noise.
We built this web application in 7 days. 2 weeks prior, we had never worked with Ruby on Rails. In addition to our custom-built machine learning algorithm and recommendation engine, the project ended with over 97% test coverage across 60+ passing tests and an average of 24 hits per line for 630 relevant lines of code.
To Launch Site on http://localhost:3000/
- Install gems.
Open a Terminal window and run:
$ bundle install
- Launch servers.
Open 3 Terminal windows. One for each of the following commands:
$ redis-server
$ bundle exec sidekiq
$ rails s
- Prepare database.
Open another Terminal window for database preparation
$ rake db:create && rake db:migrate && rake db:test:prepare
That's it! You're now ready to customize the news for you. Feel free to run rspec to check out our test suite. Comments on all code are welcome. Thanks!