# Categorizing User Sessions at Pinterest

## TL;DR

This paper presents a robust, scalable approach to automatically categorize user sessions at Pinterest using clustering and classification, enabling actionable insights for product growth and monetization.

## Contribution

It introduces a novel long-tail feature selection method that enhances the stability of session categorization amidst continuous product changes.

## Key findings

- Classifies millions of sessions daily with high accuracy
- Provides insights for product monetization and growth
- Demonstrates robustness to product experimentation and logging changes

## Abstract

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.