There are so many applications in your smartphone that uses machine learning somewhere to provide a better user experience. Social Media applications are such applications that are found in every smartphone. So to understand the use of machine learning nothing would be much better than understanding how machine learning is used by some of the popular social media applications. So in this article, I will take you through the use of machine learning in social media.
Use of Machine Learning in Social Media
To walk you through the use of machine learning in social media apps, I’m going to walk you through examples of how some of the popular social media apps use machine learning to provide a better user experience. These examples can help you understand how machine learning is used in the real world and can also give you lots of ideas for working on amazing machine learning projects. Below are examples of how some of the most popular social media apps are using machine learning.
Machine learning is like oxygen for LinkedIn. Like any business today, LinkedIn’s primary goal for using machine learning is to optimize the user experience on LinkedIn. Using data about who you’re connected to, your professional skills, and the posts you react to, LinkedIn uses machine learning to:
- recommend courses to you
- other professionals, you need to connect with or follow
- posts that might interest you
- and the jobs you should apply for
This helps LinkedIn to improve its productivity and provide a better user experience to its users.
Facebook uses machine learning to serve ads to users that help a business, as well as customers, meet their needs. Facebook’s primary goal for using machine learning in ad serving is to calculate the estimated action rate and ad quality score. To calculate the estimated action rate, a machine learning model is trained by Facebook to predict whether a person will act on the ad as desired by the advertiser. And to calculate the ad quality score, a machine learning model is trained to analyze the behaviour of people who see or ignore the ad.
Twitter’s main goal for using machine learning is to provide a better user experience by:
- generate more engagement between users
- provide the most relevant content for users
- and promote healthier conversations
So, to provide a better user experience for Twitter users, Twitter uses machine learning to determine which tweets should be recommended to a user to generate more engagement between users. To this end, machine learning models are trained to analyze and scan thousands of tweets per second to rank them for recommendation to users who are most likely to engage in them.
Instagram’s main goal for using machine learning is to recommend posts based on their relevance and timeliness. Instagram’s Recommendation System generates recommendations for you based on two main factors:
- All accounts that might interest you
- The content you would like to see
Every time you follow someone on Instagram, you see a lot of suggestions as to other personalities that you might like, and you only see posts on the topics you always like to see. This is where Instagram uses machine learning to deliver the most relevant content to its users.
So these are some of the examples of the use of machine learning in social media applications. Hope these examples helped you understand how machine learning is used in the real world and also gave you lots of ideas to work on amazing machine learning projects. Hope you liked this article on the use of machine learning in social media. Please feel free to ask your valuable questions in the comments section below.