AI in Recommendation Engines: How Businesses Keep You Engaged

In today’s world, where online experiences are highly personalized, businesses need to rely on AI to create tailored content that fits their users’ preferences. Recommendation engines are all the rage among various online platforms like e-commerce sites and streaming services, where personal preferences play a key role.

According to research, 71% of users want businesses to deliver personalized experiences, while the absence of personalization leaves 76% of consumers frustrated. That highlights just how important personalization is for companies to keep users engaged with their platforms.

In this article, we will dive into what recommendation engines are and how businesses use them to boost engagement.

What are Recommendation Engines?

A recommendation engine, as the name suggests, is a system that recommends items to users. These recommendations or suggestions are predictions about user behavior. Recommendation engines use machine learning algorithms and big data analytics to suggest items that the users will interact with. It can be a product you will put in your basket (and eventually buy) or a movie you would like to watch.

There are 5 phases in the operation of a recommendation engine: Data Gathering, Storage, Analysis, Filtering, and Refining. Based on the filtering method used, there are 3 types of recommendation engines: Collaborative filtering, content-based filtering, and the hybrid recommendation system.

In a collaborative filtering system, the suggestions are offered based on your likeness to other users on the platform. Whereas the content-based filtering system assumes that if you liked one item, you will like another similar item. It does this by using metadata and item descriptions like category, price, color, etc.

The hybrid system requires great computational power since it merges these two systems.

Making Smarter Predictions with AI

The recommendations made by the engine improve over time, courtesy of the machine learning algorithm. As more data is gathered, the system learns more about the users and their behavior and offers more accurate predictions.

Whether it is Netflix recommending your next watch or Spotify suggesting the next song, it only gets better and more accurate as you spend more time on these platforms. Online entertainment platforms like gaming sites also use recommendation engines to suggest to their users more engaging slots to keep the visitors glued to their sites. Online casinos  also use AI to personalize offers and bonuses for their players to keep them coming back for more.

Based on your activity on these platforms, the recommendation engine suggests the amount of bonus or free spins to be offered. It takes into consideration the slot machines and table games you have played at the online casino, along with many other data points, to make accurate suggestions that keep you engaged with the platform.

Applications Across Other Industries

In addition to e-commerce sites making use of these engines to predict which product a visitor is most likely to buy, recommendation engines have great applications in other industries as well.

These engines have been revolutionary in the advertising industry. Online ads are now better targeted at the potential buyer than ever before. Just like online casinos finding the right game or bonus offer for their player, advertising agencies can find the right customers to display their ads to.

When it comes to online platforms, the use of recommendation engines has been primarily to increase engagement. When businesses can predict the content you are most likely to consume, they pounce upon every opportunity to keep you hooked to their platform.

Ethical Considerations and Data Privacy

The most important conversation at this point in the development of AI and machine algorithms is that of ethical implications and data privacy. Users have a right to privacy and also to decide how their data will be used.

When a machine starts predicting user behavior accurately, a question arises: Are the choices we make truly authentic? There is a possibility of manipulation when a company has too much data that can be used to create deceptive UI/UX designs that exploit users to serve the best interest of the company.

Regulations like GDPR have helped protect the fundamental rights of individuals in the digital age. Setting a limit on the amount of data collected and how it will be processed is essential to keep our choices from being manipulated and thereby no longer remaining authentic.

Future of Recommendation Engines

The future looks exciting with some promising trends that have emerged in recent times. We now see a flux of real-time and emotion-based recommendations across various platforms.

Real-time recommendations will be a game-changer for online casinos that need to stay on top of their users’ preferences. Generative AI is making a splash across e-commerce sites by creating a summary of user reviews. In the future, this can also extend to streaming sites where users can get a dynamic summary of the show or movie in addition to the rating that is now displayed.