The Stream Blog

Best Practices for Recommendation Engines

In this blogpost I will describe how to implement a feature-rich activity feed that will make relevant and accurate personalization algorithms easier to implement. As we have already explored in previous blog posts, app personalization is linking activity feeds and user engagement data. In most cases, a well thought out feed structure provides valuable information […]

Activity Stream Personalization

Personalization comes in many flavors, and the Data Science team at Stream helps you build your own personalization engine based on your specific needs. In conjunction with our Analytics client, we can use both engagement and feed data to power and improve your app’s experience using cutting edge Machine Learning algorithms. Here are some of […]

Example Ranking Methods for Your Feeds

In this short tutorial we will show you how to use Custom Ranking for your activity streams and news feeds. By default all feeds on Stream are ranked chronologically. Custom ranking allows you to take full control over how your feeds are sorted. Some common use cases include: Showing popular activities higher in the feed […]

An Introduction to Contextual Bandits

In this post I discuss the Multi Armed Bandit problem and its applications to feed personalization. First, I will use a simple synthetic example to visualize arm selection in with bandit algorithms, I also evaluate the performance of some of the best known algorithms on a dataset for musical genre recommendations. What is a Multi-Armed Bandit? Imagine […]

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 […]