Understanding The Basics Of Recommendation Systems

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You’ve come across a recommendation system already while using your electronic devices. Maybe you’ve encountered it when scrolling through social media, a streaming service, or shopping online.

For instance, in the case of streaming platforms, the ‘item’ referred to is a movie or series based on the kinds of shows you’ve previously liked and watched. Another example is an e-commerce platform that recommends products to you based on what you’ve bought recently and given high ratings.

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With that, what are recommendation systems exactly? Continue reading below to learn more.

An Overview Of Recommendation Systems

basics of recommendation system

A recommendation system refers to a subclass under information filtering systems that predict a user’s possible preference for a specific item. Essentially, it’s an algorithm-driven by data, even artificial intelligence and machine learning, that provides relevant content and products to users.

Recommendation systems are here to stay, especially with the crucial role in marketing and sales for online enterprises, especially e-commerce. They have significantly aided businesses in providing personalized and customized user experiences.

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And this, in return, has been known to positively influence purchase decisions if done right, as effective recommendation systems can make users keep returning to the platform, staying there for longer, and purchasing more.

Suppose you’ve taken an interest in recommendation systems. Or perhaps, you’re hoping to implement it in a personal project, or you’re looking to build one for a startup. If you’re hoping to create a recommendations system with AI blueprints, consider checking out this post by cnvrg after learning the basics from this article.

That being said, here are some insights explaining the basics of recommendation systems.

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The Kind Of Data Used For Recommendation Systems

For recommendation systems’ algorithms to work, they gather and process the following data about the users: demographic, behavior, and product attitude.

  • Demographic: This data refers to customer data and personal information such as age, gender, location, etc. This data helps businesses in providing relevant content to their target market.
  • Behavior: This refers to information about the user’s engagement and activity within the platform. For example, this will refer to the genres you often choose, the movies you repeatedly watch, and your ratings on streaming platforms.
  • Product Attitude: This refers to the information about the product, such as its class or genre. For restaurants, for instance. This could mean products are categorized according to cuisine. If you prefer a particular cuisine, such as a Japanese food delivery platform, it may recommend more Japanese food.

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How Recommendation Systems Understand Relationships

Recommendation systems must understand relationships to gain insight and better understand customers. The kinds of relationships it looks into are user-product, product-product, and user-user.

User-product essentially refers to a user’s preference for a particular product. For example, you recently bought a badminton racket. This online activity can trigger other e-commerce platforms to recommend more sports-related items that you may be interested in.

Next is product-product, which refers to how certain items are related, such as a baseball bat with a baseball cap and glove. Or a movie or book recommendation from the same genre. And lastly, you have user-user, which refers to users having similar preferences for a particular product or service.

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How Data Is Provided To Recommendation Systems

As mentioned, at the heart of recommendation systems are algorithms. For these algorithms to work, they must be fed data. Mainly, there are two ways you can do this: explicit and implicit.

Explicit refers to when users directly express their preference on the platform through ratings, feedback, and interactions such as likes and following. Meanwhile, it implicitly refers to when a user interacts with a product. This infers a user’s behavior and product preference when they click, view, or purchase.

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Balancing Privacy And Personalization

Having established the power of a recommendation system and how it works, we must also discuss its ethical aspect. This is especially with the growing awareness and negative cases regarding how it can violate users’ right to privacy, specifically data privacy.

An instance of such negative cases is how user data are being sold to advertisers to improve their targeting algorithm for their online ads. Unfortunately, in some cases, this is being done without the consent of the data’s owners.

And so, in response to this, governments, both local and international, are drafting laws and policies to combat this and penalize violators.

That said, it’s essential to ensure that the user data for your recommendation systems are secure and you know how to handle them wisely.

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Conclusion

Personalization and customization are crucial for improving the user experience of one’s audience. For businesses, this also means generating more sales on their online platforms and gaining more customers. And through recommendation systems, you can achieve this as it tailors the experience of users based on the data they provide through their interactions.