What are Recommendation Systems & Types of Recommendation Systems | Recommendation Systems Explained- Codexashish

In this article, we are going to see what is a recommendation system, the use cases of recommendation systems, why we use recommendation systems, and what are the types of recommendation systems. So without wasting any time let's start this article with a short intro about recommendation systems in Machine Learning.

    As we know that Netflix uses a recommendation system to recommend movies and web series on the behalf of user interest and Youtube also uses a recommendation system to recommend videos so that users can spend more time on their platforms. The use cases of recommendation systems have been increasing consistently and there could be no better time than now to dive deeper into this excellent machine learning technology so that we can also utilize this technique in the right direction.

    What Are Recommendation Systems in Machine Learning?

    Recommendation systems are like filtering systems that attempt to predict the rating or preference a user might give an item. In simple terms, it is an algorithm that suggests relevant products, advertisements, or articles to users. There are many different things the system can recommend, e.g. Movies, books, news, articles, jobs, advertisements, etc. Netflix uses a recommendation system to recommend movies and web series to its users and YouTube recommends various videos.

    Four Phases are used to Process data in the Recommendation System:

    • Collection
    • Storing
    • Analyzing
    • Filtering

    Recommendation System Use Cases

    • Analyzing:-

    The recommendation system can find any items similar to user engagement data after analyzing the whole data.

    • Personalized Content:-

    It helps to improve on-site experiences by making dynamic recommendations for different types of audiences, just as Facebook and Netflix do.

    • Better Product search experience:-

    It helps to categorize products based on their characteristics and it can categorize your products.

    Why the Recommendation system?

    • Help users find interesting videos on different media
    • This helps users find articles that interest them
    • Help suppliers of goods deliver their goods to the right users
    • Help people find nearby friends
    • The product identity is most relevant to consumers
    • Help websites increase user engagement

    Types of Recommendation Systems

    There are mainly four types of recommendation systems that are used nowadays. We will learn all types one by one with good examples and a short description.

    No.1: Popularity-Based Recommendation System

    So first is the popularity-based recommendation system which is one of the easiest recommendation systems nowadays. It's a kind of recommendation system that works on the principle of popularity and/or whatever is trending. The system checks the trending or most popular products among users and recommends them directly.

    For example, if a product is frequently purchased by most people, the system knows that this product is the most popular, so every new user who has just signed it, the system will recommend this product to that user as well, and the chances are high, that new users will buy this too. 

    You can create this recommendation system easily with the help of python programming and pandas, NumPy libraries. This recommendation system is based on the most popular or most seen products, for example, if you have a website and you want to get the most viewed article then this recommendation system is used. The popularity-based recommendation system is used on every website, you can see this with tag names like the most popular article. Youtube studio also uses this system to show the most popular videos in the last 28 days.

    No.2: Content-Based Recommendation System

    This is another type of recommendation system that works on a similar content principle. When a user watches a video, the system will search for other videos with similar content or the same genre as the video the user is watching. There are several basic attributes that are used to calculate similarity when searching for similar content. Content-based filtering methods are based on product descriptions and user preference profiles. In this recommendation system, products are described using keywords and user profiles are created to express the types of items that users prefer.

    For example, if you are reading articles related to data science then the system will recommend more articles related to data science and data analytics. And you can see this type of recommendation system on many websites like media and others. They used to show used-based articles so that users can spend more time on their platforms. This is how Content-based recommendation systems work.

    No.3: Collaborative-Based Recommendation System

    It is considered to be one of the most intelligent recommender systems based on similarities between different users and also widely used elements such as e-commerce websites and online movie websites. The collaborative filtering method is based on the collection and analysis of user behavior data. This includes users' online activity and predicting what they will like based on similarities with other users.

    For example, if A person is searching more about Data Science, DevOps, and Machine Learning and B Person is searching about Deep Learning, Machine Learning, and AWS then it is possible to recommend Deep Learning to A person and DevOps to B person. It is how collaborative-based recommendation systems work.

    Two kinds of Collaborative Based Techniques used are:

    • User-User collaborative Based
    • Item-Item collaborative Based

    No.4: Hybrid Recommendation Systems

    The hybrid recommendation system recommends products that use collaborative, content-based filters to offer a wider range of products to customers. This recommendation system is predictive and is intended to provide more accurate recommendations than other recommendation systems in machine learning techniques.

    For example, Netflix is one of the companies that use hybrid recommendation systems to recommend movies to their viewers. First Netflix sees users' interest on the behalf of the user searching history and users' previous watching history after this data they used to collect similar types of users that also watch that kind of movie. After collecting this data they recommend movies and web series to every user.

    Best Recommendation Systems available on the internet:

    • Netflix relies on a recommendation system to provide movie recommendations
    • Facebook uses a recommendation engine based on DL and Neural Network
    • Amazon leverages a recommendation algorithm to recommend products and many more
    • YouTube also uses a recommendation system to recommend videos or to provide news
    • LinkedIn relies on a recommendation system to provide jobs, news, or any other

    I would like to end the blog by stating that the recommender system has changed the whole scenario by making it easier for the user to choose the options and interests they want. It recommends customized content for users. There are various other platforms currently used by recommendation systems.


    In this article, we have covered many topics related to recommendation systems such as what is a recommendation system, its types, and many techniques used in a recommendation system. But there may be many technological advances to be expected in the future as there are many challenges for recommender systems.

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    Thank you for reading this blog. I wish you the best in your journey in learning and mastering Machine Learning.

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    Ashish Yadav

    Hi, I am Ashish Yadav, The founder of the codexashish.com website. I am a Data Analyst by profession and a Blogger, and YouTuber by choice and I love sharing my knowledge with needy people like You. I love coding and blogging.

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