Fast Recommendations for Activity Streams Using Vowpal Wabbit

The problem of content discovery and recommendation is very common in many machine learning applications: social networks, news aggregators and search engines are constantly updating and tweaking their algorithms to give individual users a unique experience. Personalization engines suggest relevant content with the objective of maximizing a specific metric. For example: a news website might want to increase the number of clicks in a session; on the other hand, for an e-commerce app it is very important to identify visitors that are more likely to buy a product in order to target them with special offers. In this post I will explore some techniques that can be used to generate recommendations and predictions using the amazingly fast Vowpal Wabbit library. Make sure that you have installed scikit-learn and Vowpal Wabbit's Prerequisite Software.