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Decoding the Popular Instagram Recommendation System - Everything You Need to Know

Gokul
By
Gokul
• 
5 min
Instagram recommendation system


With roughly 1 billion monthly active users, Instagram is rapidly growing and evolving as a  social media giant. The platform attracts new and existing users with its efficient AI-model algorithm and recommendation system. Instagram's AI algorithm recommends content out of billions of options in real-time with its efficient data and engineering expertise.

Instagram’s AI recommendation system aims to surface content according to the intent of the user, and this makes us curious enough to ask:

  • Which posts go to the top of the newsfeed, and in which order 
  • In what order should the Reels and IGTV videos  show up, in the feed
  • Which posts are to be featured on the Explore tab

Let’s dig deep into the Instagram AI recommendation system that has made it so popular:

How does the Instagram Recommendation System Collect Data?

Instagram's system design requires an AI model recommendation system like TikTok follows, a ranking system that taps into users' interactions to position posts on your feed. The main goal of the Instagram AI recommendation system is to fit a user's satisfaction. And for this, the system requires data from different dimensions. The data gathered is defined below:

User Data

The Instagram data collection includes the bio, career, age, gender, demographics of every user. It tries to understand user-interests by collecting the user-activity information like the previously viewed posts and user-interaction (most liked, shared, or commented posts). Instagram's data collection process includes the time a person is opening the app, let say in the morning, someone might be wanting to see stock market update posts, but at night the person prefers watching some funny reels. There is also an option to dislike a post, Instagram gives you an option to not repeat posts of that category. This data is also recorded by the Instagram algorithm.

Option where you can restrict posts
Source

Creator Data

Instagram's data collection, assembles data about the person who has posted the video, the most preferred genre in which these creators usually create videos, let's say sports, fitness, dance, fashion, etc. This information is acquired to provide reliable recommendations to a user. Instagram AI model recommendation system also offers an option to make a creator profile for users above 10,000 followers. And while providing recommendations, these profiles are preferred over others.

Scenario Data

Instagram also collects some extra information about the post like objects in a video or image, the scenario, background, and activities in the video or photo, for example, it could be animals, could be people dancing, travel, etc. Such information collected about the post helps identify the right audience set for this reel/post.

Content Data

Instagram is a platform with primarily user-generated content. Each content has its traits, and the algorithm should identify and distinguish them to provide an accurate recommendation. Instagram's data collection includes the kind of filters in a post, the effect, frames, the type of audio used if it's a slow or fast one. The other content gathered is captions, tags, and hashtags to analyze a post.  

Key Features of Instagram's Recommendation System

The Instagram system design includes signals and the information collected by the Instagram AI recommendation system tackle the ranking of the high volume of photos and videos uploaded daily. Along with this, some features make the recommendations of Instagram unique. Let's look at some of them:

Cold-start problem

 Most AI recommendation systems face the issue of serving relevant recommendations when a new user signs up to the app, which is technically known as the cold start problem. As there is no previous history or data from the user to recommend relevant content, the Instagram AI recommendation system deals with the cold start problem in the following two ways.

For the users whose immediate engagement graph is sparse, the Instagram AI model recommendation system generates content  for them by evaluating their one-hop and two-hop connections. Example: If a user X hasn’t liked a lot of content, the system evaluates the accounts followed by X and then accounts followed by X’s followers/following accounts.

For extremely new users, the Instagram AI recommendation system typically gets them started with trending media items, and then the system evaluates parameters based on their initial response.

New user installing Instagram
Source

Removal of abusive content

There is a major impact of abusive content - whether it's racist, sexist, homophobic, or any other kind of abuse on people. The Instagram algorithm offers a number of tools to help a user to control abuse or misleading content. Instagram tries to identify such content using its AI engine, by taking frames of the videos or by the audio used. So most of the abusive content gets filtered by the AI engine only.

And if content is abusive, you can also report or block the account. With this feature, whenever you decide to block/ report someone on Instagram. And when the Instagram AI recommendation system notes this, immediate action is taken. With this, you also have harassment policies, which already prohibit people from posting abusive content or  from repeatedly contacting someone with offensive content.



Option to report offensive content
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Avoiding content repetition

One of the inherent challenges with Instagram AI model recommendation systems is limiting your experience after a point in time. Instagram's system design handles it like it generally does not recommend more videos made with the same sound or by the same creator. Instagram AI model algorithm enables various categories of content like food, destinations, culture across the globe. The recommendation system gives users the experience to improve the quality of the app every time. For this, the user needs to clear and reset the page every time to avoid repetitive content.

Distillation model

There are more than billions of Instagram accounts, and to maximize the content for each request, Instagram AI recommendation system has a ranking distillation model that helps preselect candidates. The top-ranked accounts from the distillation model helps to identify content for the ranking.

​​Instagram AI model recommendation system doesn't blindly recommend content just because the creator is popular. A video is targeted to a larger audience based on the content it has, irrespective of how popular a creator is.

The Instagram AI recommendation system creates "seed" accounts. Instagram takes a sample of 500 from the seed accounts and sends them to the next stage. With further ranking, the system filters 150 candidates from 500 based on user interaction and intent. The second stage uses a lightweight neural network to pick 50 quality candidates from 150. And content from the 50 candidates is then evaluated, the result of which appears in the explore and reels page.


Candidate Generation by distillation model
Source


Wrapping Up

One of the best parts of the AI model Instagram recommendation system is the ongoing challenge of finding new and updated ways to help the audience discover the most exciting and relevant content on Instagram. 

So whether you are a streaming platform or a website, an AI-driven recommendation system can boost conversions and engagement, just like Instagram does.
Looking for an efficient and accurate recommendation system like Instagram? Try Argoid.

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