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4 Great Platforms That Use Recommendation System

Ankit Jena Published on : 02 March 2022
Recommendation system


The Recommendation system might have been a complex thing or luxury for companies in the past, but now it has become a necessity to most of the companies. While it has revolutionized the ecommerce and streaming industry, many other industries also seem to be making the most of this technology. 

Attaining heights in marketing is quite challenging. There are a very few methods to increase sales without much effort. Recommendation system is that tool which can help you achieve a new height in marketing. AI-based recommendation systems are able to improve the user experience, give better engagement and increased CTR ratios or higher sales. Integrating a recommendation engine with your website or application also helps you get recurring additional sales.

It provides the shortest path that reduces the effort for both you as well as your customers. It provides an appropriate option to the end user that reduces their time searching on your website and thus provides huge customer satisfaction.    


Recommendation system


AI-powered recommendation engines provide a great method to re-engage customers. Discounts and coupons coupled with recommendation solutions re-engage customers and increase customer’s probability of conversion.

Now, let’s know the name of some renowned platforms that use recommendation systems. Let’s get started…


4 Renowned Platforms that Use a Recommendation System



Amazon has solely emphasized and demonstrated the retail value of Artificial intelligence. Amazon uses recommendation engines as a prime marketing tool all through the website. When an end-user clicks on the recommendations, the link redirects the user to another page on the website that contains even fine filtered data along with subject area, product types, and ratings of previous products and purchases. The user can also understand why this particular product has been recommended to him or her.

The AI-powered recommendation engine uses algorithms to personalize the online store according to the taste and preferences of each customer. The store instantly changes according to the interest of the consumer. Amazon’s recommendation system uses item-to-item collaborative filtering.

This type of filtering connects each user’s purchase to similar items and fabricates a recommendation list for them.



According to a journal written by Netflix administrative staff Carlos A. Gomez-Uribe and Neil Hunt, the recommendation engine saves the company around $1 billion dollars annually. And this revenue helps them add more content which viewers will continue to view, offering them a good Return on investment. Netflix has also discovered all through these years that adding a recommendation engine adds an incredible value to the subscribers by offering them a personalized experience.



Spotify uses recommendation engines in an innovative way through Popular Discovery Weekly and this is popularly known as Release Radar. This feature updates personal playlists on a weekly basis so that end users won’t miss out newly released music by popular artists. Through the section – RELEASE RADAR, Spotify wanted to create the easiest method for users to find all newly released music according to their preferences.



YouTube uses a recommendation system to create personalized recommendations helping users quickly and easily find videos that are pertinent to their preferences. Valuing user engagement, YouTube constantly struggles to reflect each user’s activity based on the website and at the same time spotlight a broad range of available content. The recommendation engine of YouTube first collects and collates the information on users’ watch history and utilizes collaborative filtering to recommend videos to the end users.


AI-powered recommendation engine – Alie

Alie is an AI-powered recommendation engine that delivers personalized experiences to capture user’s attention. From optimizing pricing to suggesting captivating content, Alie has the potential to help businesses grow and retain their users. Start a 14-days free trial and engage users by offering them relevant contents.


Recommendation system

Written by: Ankit Jena

Ankit is Content Writer for Muvi’s Marketing unit. He is a passionate writer with 5+ Years of Experience in Content Creation And Development. In his past time, he likes to dance, play football and google various things to quench his thirst for knowledge.

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