Introduction to Recommendation Systems
Here’s everything that you should know about recommendation systems as a Data Scientist.
Almost every app or website you visit today shows you what may interest you the most. For example, if you’ve been using YouTube for a long time, you must have seen content from your favourite creators or the type of videos you like to watch every time you visit YouTube. It is nothing more than a recommendation system. Recommendation systems are one of the most widely used applications in data science, and as a data scientist, you should know everything about recommender systems. In this article, I will therefore give you an introduction to recommendation systems in data science.
If you take a close look at every app or website you visit, you’ll understand how every recommendation system works. When you visit an application or a website for the very first time, it will probably show you its most popular content among its various users, but once you keep visiting it for a while, it starts recommending the items you should read, buy, watch, listen or spend your time for. This means that recommendation systems are based on three important factors:
The combination of users, content, and ratings creates two different approaches for creating recommendation systems, which are:
Now let’s go through both these approaches to understand how a recommendation system works.
The content-based approach is based on user data or content (by content I mean books, articles, videos, music, products to buy, or whatever you get recommendations for). User data or content is used to target a new user that falls under the same category of users. For example, all the readers who are reading this article are learning about data science, so if the data of new users have the same features as other users reading this article will see this article in the recommendations.
Collaborative filtering is very sophisticated compared to content-based recommendation systems. They are based on the ratings or comments given by the user and their purpose is to predict the ratings for each content and each user. For example, if most people rate the latest iPhone with 5 stars, in this case, the recommendation system will predict how you will rate this offer based on data about your interests and will only show you this offer if it predicts positive feedback.
Collaborative filtering is more accurate than content-based recommendation systems, as it works on large datasets with more computational power. Most of the websites that use collaborative filtering ensure that their recommendation system is updated every day by retraining the model and updating the data about the users.
Recommendation systems are one of the most widely used applications in data science, and as a data scientist, you should know everything about recommender systems. I hope you have now understood what are recommendation systems and how they work. I tried to keep this article in plain English so that you can easily understand the concepts behind a recommendation system. I hope you liked this article on an introduction to the recommendation systems. Feel free to ask your valuable questions in the comments section below.