Top Machine Learning Recommendation Systems We Use in Daily Life
Global Tech Outlook features some of the top machine learning recommendation systems for daily life
Every day we apply some accurate recommendations from different companies to purchase some essential products, services, watch movies, series, or listen to songs of the recommended genre. You may have wondered how these apps know you better than anyone else! Machine learning recommender systems are known for collecting enormous sets of data to analyse and recommend accurate preferences to customers with the help of machine learning algorithms. These algorithms study what customers like, love, hate, or any emotion related to movies, articles, products, services, music, games, makeup, and so on. These recommendation systems in machine learning entice users to follow their recommendation list for mental or emotional satisfaction through personalized services. The way the users prefer these recommendation lists, it helps companies to improve customer engagement, sales, and brand loyalty in the cut-throat competitive market. Let’s explore some of the top machine learning recommender systems we use in daily life.
Top machine learning recommender systems we use in daily life
Popularity-based Recommendation System
In the popularity-based recommendation system, the machine learning algorithms work with the popularity principle— the hottest trends in the market at present. This machine learning recommender system enables users to be aware of the most popular or trending products and services present in the world to eradicate the feeling of FOMO (Fear of Missing Out) to the tech-savvy population. There is no need for analysing the historical data of users for accurate recommendations. Netflix, Amazon, Flipkart, Google News, YouTube, and many more apps recommend popular trendy products to users to use, watch, read, and listen to.
Classification Model of Recommendation System
The classification model provides a list of products and users to predict whether those users will prefer these products or not. The implementation of the model is a difficult and tiring task to collect enormous volumes of data about different users and different products. But some companies use this to study the market demand and gain a competitive edge to stand in the market for the long term.
Content-based Recommendation System
In the content-based recommendation system, the machine learning algorithms work with similar content principle. This machine learning recommender system analyzes the favourite or preferred kind of content users like. Then the machine learning algorithms check out other products and services that consist of similar tastes or genres of content to create a recommendation list for users. We use a content-based recommendation system in daily life with Netflix, Amazon Prime, and many more.
Popular apps that use recommendation systems in machine learning
Amazon: Amazon is a very popular tech company that users prefer because of its recommendation list at the right time. It uses the machine learning recommendation system as a targeted marketing tool filtered with popularity-based as well as collaborative filtering through core area, product, and product ratings.
Netflix: Netflix has favourite recommendation lists for viewers that consist of the right genres for avid cinema or series lovers. The company utilizes a recommendation system through personalized diversity to recommend the top ten lists for different users with personalized approaches.
Spotify: Spotify also uses a recommendation system in machine learning with artificial intelligence to recommend a list of the popular Discover Weekly Playlist. It uses the popularity-based recommendation system and collaborative filtering for helping music lovers not to miss any current popular songs.
YouTube:YouTube is famous for recommending similar content in a long list— series of dog videos, song series of a preferred artist, and so on. It utilizes machine learning algorithms in the content-based recommendation system to seek videos relevant to the interest of users.