  Different users can use a given Internet application in many different ways.
The ability to record detailed event logs of user in-application activity
allows us to discover ways in which the application is being used. This enables
personalization and also leads to important insights with actionable business
and product outcomes.
  Here we study the problem of user session categorization, where the goal is
to automatically discover categories/classes of user in-session behavior using
event logs, and then consistently categorize each user session into the
discovered classes. We develop a three stage approach which uses clustering to
discover categories of sessions, then builds classifiers to classify new
sessions into the discovered categories, and finally performs daily
classification in a distributed pipeline. An important innovation of our
approach is selecting a set of events as long-tail features, and replacing them
with a new feature that is less sensitive to product experimentation and
logging changes. This allows for robust and stable identification of session
types even though the underlying application is constantly changing. We deploy
the approach to Pinterest and demonstrate its effectiveness. We discover
insights that have consequences for product monetization, growth, and design.
Our solution classifies millions of user sessions daily and leads to actionable
insights.
