Take advantage of user data to create intelligent applications. This talk will focus on data mining to create complex application behavior and gain insight into the patterns and habits of your users. Examples of these techniques can be seen with recommendation systems like those created by Amazon, Netflix, last.fm, and others. Additional examples include spam filtering systems for email or comment filtering provided by Akismet.
We will focus on techniques for gathering data, specific gems and plugins for performing various data mining and machine learning tasks, and performance issues like how to distribute the work to separate servers. Theory in this talk will be light and the specific algorithms will only get a mention by name. We’ll be looking at real world Ruby and Rails code examples for building recommendation, ranking, and classification systems.
Paul Dix has been working with Ruby and Rails since 2005 when he first started attending meetings at NYC.rb. Paul was a speaker at GoRuCo 2007 where he presented on the topic of document classification. He is also the author of the Basset Ruby Gem, which provides an API for various machine learning tasks. Paul has worked in companies large and small as a consultant, developer, network engineer, and software tester. The big names include Google, Microsoft, McAfee, and Air Force Space Command. He currently works for Mint Digital where he contributes to various Rails applications for clients and Mint’s Rails application platform. In addition to his consulting, Paul is working on a new startup that makes use of collective intelligence techniques. He is also currently a student at Columbia University where he studies topics in machine learning, natural language processing, information retrieval, and search.</p>