AI Is Creating Better Content Recommendations — Here’s How

Think about the last time you took a break from the world of blockchain and closed your crypto trading app to watch a new Netflix series. How and why did you choose that exact title? Did you find it through the platform’s search feature or was it a personalized recommendation? Chances are it was the second option, as 80% of Netflix viewer activity is driven by personalized recommendations.

Personalized content recommendations are increasingly important in predicting people’s preferences and increasing customer retention through an improved user experience. But what makes the Netflix Recommendation Engine (NRE) so accurate when offering personalized content? Artificial intelligence and machine learning algorithms are an important part of Netflix’s secret sauce.

In a nutshell, artificial intelligence (AI) refers to a computer or machine’s ability to perform a task that is generally executed by humans. As the computer science of creating intelligent machines evolved, so too have the use cases of AI and machine learning — and generating personalized content recommendations by analyzing user behavior has become one of its most important functions. Below, we’ll break down how AI is creating more relevant content recommendations and how we’re benefiting from personalized suggestions — both in the world of cryptocurrency and beyond.

How AI Is Enhancing Content Recommendations

The content and product recommendations you see on some crypto media sites, streaming, and e-commerce platforms are created through recommendation engines. Recommendation engines are currently among the most popular applications of AI. In a nutshell, these are machine learning algorithms that generate recommendations based on users’ individual preferences, browsing history, and an array of additional user data. The main goal of a recommendation engine is to find relevant suggestions for people, enhancing their experience.

Recommendation engines are widely used across multiple industries, like e-commerce, blockchain, and digital media, to influence customers towards a given item or piece of content. Today’s tech giants are employing AI-based recommendation engines to suggest content, products, and services based on user data. Amazon, for instance, offers you item recommendations based on your shopping history and previously viewed products. On the other hand, video streaming giant YouTube leverages its AI-based recommendation engine to gain a better understanding of its audience and offer more relevant video recommendations.

Adding the underlying technology behind cryptocurrencies and NFTs in the mix, blockchain combined with AI are reinventing the media industry through more transparency and advanced insights — helping media companies and content creators get compensated for their work. And while blockchain plays an important role in digital rights attribution, AI is responsible for generating relevant content recommendations for the right audience.

But, how does artificial intelligence accomplish all this and generate improved content recommendations? Since AI has an astonishing capacity to gather and interpret large sets of data, the first step is collecting and analyzing user information. Depending on the type of content and platform, AI algorithms will usually analyze a user’s previously visited pages and the time spent on them, items and content the users clicked on, the type of content they consumed, and how long they remained engaged. Social media giants, like Facebook, even employ AI algorithms that track mouse movements and how long users hover over a specific item.

Once the user data has been collected and analyzed, AI algorithms apply it to create a set of statistics on the historical probability of a user’s potential actions. So if 80% of users who previously purchased The Bitcoin Standard then read The Basics of Bitcoins and Blockchains, the AI will suggest the former as the first recommendation for similar profiled users who read The Bitcoin Standard.

Finally, combining the insights from the user data and the probability of user actions, the AI algorithm generates a set of relevant, highly-personalized content recommendations for each individual user.

The Benefits of AI-Powered Recommendation Engines

AI-driven recommendation engines bring an array of benefits for both the users and platforms. AI can help understand the behavior of users and curate more relevant and personalized recommendations — which improve the overall user experience and decrease the time spent looking for content that appeals to the user.

As for the platforms employing AI for content and product recommendations, there are even more benefits. For e-commerce giants, AI can increase conversion rates and generate more sales revenue by maximizing the potential value of individual customers with relevant recommendations. According to a study by McKinsey, up to 35% of Amazon’s sales revenue is generated through its AI-based recommendation algorithm — a staggering third of their total sales revenue.

Streaming giant Netflix was among the first companies employing AI to offer more relevant content recommendations for viewers. According to a paper published by Netflix executives, Carlos Uribe-Gomez and Neil Hunt, the platform’s AI recommendation engine saves $1 billion each year. By having some of its niche titles surface through personalized recommendations — which may otherwise go unnoticed — Netflix maximizes the value of this content. The paper also disclosed that the take rate for the top personalized recommendations was three to four times higher than simply providing the most popular title recommendations.

AI-generated content recommendations are essential for today’s tech-savvy internet users and crypto enthusiasts who want to see the content that matters to them. AI and machine learning-based algorithms enable companies to understand, anticipate, and fulfill their customers’ expectations while reaping the cost-saving and revenue-increasing benefits of the technology. As the world is embracing the user-oriented, highly-personalized ethos of blockchain and Web 3.0, AI-based content recommendations will become a vital element of the Semantic Web.

Disclaimer: This is a press release. KryptoMoney does not endorse and is not responsible for or liable for any content, accuracy, quality, advertising, products, or other materials on this page. Readers should do their own research before taking any actions related to the company. KryptoMoney is not responsible, directly or indirectly, for any damage or loss caused or alleged to be caused by or in connection with the use of or reliance on any content, goods, or services mentioned in the article.