Personalization Introduction

LAST EDIT Jul 19 2024

Introduction to Personalization and Analytics


Personalization is a powerful feature. It enables you to leverage machine learning to optimize what's shown in the feed. The 5 most common use cases are:

Famous examples of discovery feeds are: Instagram's explore section, and Pinterest's main feed. Edge rank is used by Facebook and LinkedIn. Stream uses 3 data sources for personalization:

1. Feeds & Follows


The best way to understand how feeds and follows work is to try our 5-minute interactive tutorial.

2. Analytics


The purpose of analytics is to track which activities a user is looking at and what they are engaging with. Basically, you want to track everything that indicates a user's interest in something. Common examples include:

  • Clicking on a link

  • Likes or comments

  • Sharing an activity

  • Viewing another user's profile page

  • Search terms

The events and data you want to track are often different than what you traditionally track in Google Analytics or Mixpanel. Stream's analytics is designed to run alongside your existing analytics solution.

3. Collections


Collections enable you to sync information to Stream that's not captured by analytics or feeds. Common examples include user profiles and product information.

Personalized Feed


Personalization is custom for every enterprise customer of Stream. The SDK exposes a flexible GET method to enable you to make authenticated get requests to personalization easily:

Follow Suggestions


Stream makes it easy to add follow-suggestions to your app. Simply make the API call shown below to retrieve a list of follow suggestions.

Follow suggestions are disabled by default. Contact support so we can enable it for your organization.

By default the follow-suggestions are based on a standardized graph analysis algorithm. Our data science team can work with you to create a customized algorithm for your app.

Contact to learn more.