Personalization Introduction

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:

// Read the personalized feed for a given user
const params = { user_id: '123', feed_slug: 'timeline' };

client.personalization.get('personalized_feed', params).then(
 (resolution) => console.log(resolution["response"]["data"]),
 (rejection) => console.log(rejection)
);

// Our data science team will typically tell you which endpoint to use
const params = { user_id: '123', source_feed_slug: 'timeline', target_feed_slug: 'user' };

client.personalization.get('discovery_feed', params).then(
 (resolution) => console.log(resolution["response"]["data"]),
 (rejection) => console.log(rejection)
);

# Read the personalized feed for a given user
client.personalization.get('personalized_feed', user_id=123, feed_slug='timeline')

# Our data science team will typically tell you which endpoint to use
client.personalization.get('discovery_feed', user_id=123, source_feed_slug='timeline', target_feed_slug='user')

# Read the personalization feed for a given user
client.personalization.get('personalized_feed', user_id: 123, feed_slug: 'timeline')

# Our data science team will typically tell you which endpoint to use
client.personalization.get('discovery_feed', user_id: 123, source_feed_slug: 'timeline', target_feed_slug: 'user')

// Read the personalized feed for a given user
$client->personalization()->get('personalized_feed', [
  'user_id' => 123,
  'feed_slug' => 'timeline',
]);

// Our data science team will typically tell you which endpoint to use
$client->personalization->get('discovery_feed', [
  'user_id' => 123,
  'source_feed_slug' => 'timeline',
  'target_feed_slug' => 'user',
]);

// Read the personalization feed for a given user
client.personalization().get("personalized_feed", new ImmutableMap.Builder<string, object>()
    .put("user_id", 123)
    .put("feed_slug", "timeline")
    .build());

// Our data science team will typically tell you which endpoint to use
client.personalization().get("discovery_feed", new ImmutableMap.Builder<string, object>()
    .put("user_id", 123)
    .put("source_feed_slug", "timeline")
    .put("target_feed_slug", "user")
    .build());

// Read the personalization feed for a given user
	resp, err := client.Personalization().Get(context.TODO(), "personalized_feed", map[string]any{
		"user_id":  123,
		"feed_slug": "timeline",
	})
	if err != nil {
		panic(err)
	}

	// Our data science team will typically tell you which endpoint to use
	resp, err := client.Personalization().Get(context.TODO(), "discovery_feed", map[string]any{
		"user_id":     123,
		"source_feed_slug": "timeline",
		"target_feed_slug": "user",
	})
	if err != nil {
		panic(err)
	}

// Read the personalized feed for a given user
var params = {'user_id': 'john-doe', 'feed_slug': 'timeline'};

client.personalization.get('personalized_feed', params: params);

//Our data science team will typically tell you which endpoint to use
params = {
 'user_id': 'john-doe',
 'source_feed_slug': 'timeline',
 'target_feed_slug': 'user'
};

await client.personalization.get('discovery_feed', params: params);

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.

suggestions = client.personalization.get('follow_recommendations', target_feed_slug='user', user_id='123', source_feed_slug='timeline')

print(suggestions)

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 sales@getstream.io to learn more.