When it comes to AI, most people are focused on technology. The implementation of AI has led to all sorts of exciting new projects, from AI-created art platforms to highly complex medical procedure imaging. In other words, the sky’s the limit—and many people are interested in seeing just how high AI can fly.
But one of the most impactful ways that the average person interacts with AI is through recommendation algorithms. These programs are designed to identify patterns in a person’s use history to make personalized suggestions on what they should use, buy, or try out next. Though basic, it has revolutionized many sectors.
The easiest to study is entertainment. After all, almost everyone in the world has a preferred platform for doing things like playing music, scrolling social media, and even shopping. While the average consumer might not realize it, AI is behind the recommendations that they click on.
But just what is this technology doing behind the scenes? And why is it increasingly important for a wide range of entertainment sectors? Let’s take a closer look, starting out with music.
Music
There’s a lot of hype about AI helping artists create their next big hit. But in reality, AI already plays a crucial role in helping expose artists to new listeners. Depending on which music app you use (whether purchasing songs or streaming them), you probably have access to a ‘recommended’ or, in the case of Spotify, a ‘discovery’ playlist.
Recommendation algorithms closely study your favorite songs. They track things like BPMs, genres, decades, and more to uncover each user’s listening pattern. Based on the data, these apps recommend brand-new artists, albums, and songs—potentially exposing you to your next favorite thing.
Casinos
In the casino industry, AI-driven recommendations are a pivotal way for players to discover new games. Let’s use the example of live roulette, a straightforward game that’s popular around the world. While the focus is on the spinning wheel and placing bets, there’s quite a bit of range for players to choose from—from live dealer roulette to French variations.
Recommendation algorithms can help you find the lesser-known variations of classic games. This helps keep the gaming experience fresh and novel, even if you’ve already played roulette hundreds of times. In other words, it’s about offering something new while building on what players already want.
Video Streaming
Video streaming is a bit more complex in terms of recommendation algorithms. In the case of music, programs study concrete data. In the case of casino games, suggestions are based on playing history. But with video streaming platforms, many algorithms rely on other users.
That means that your recommendations on video streaming platforms (from YouTube to Amazon Prime) are based on your previous watching history along with the types of titles that other users enjoyed. You’re segmented into a certain demographic which is based on aggregate data from other users, meaning they’re swaying your recommendations to a certain degree.
Social Media (and Shopping)
Social media is one of the most transparent ways for the average user to understand how a recommendation algorithm works. In fact, this was how many people first learned that AI was behind their social media feed, crafting personalized recommendations based on history, location, gender, and more.
But social media algorithms are even more tangled than others on this list. That’s because there’s a massive element of advertising. Though every single entertainment industry relies on advertising for profits, social media has become increasingly dependent on ad revenue. That means that its algorithms are tightly focused on identifying your ‘type’ in order to make tailored eCommerce suggestions.
In fact, this has blurred the lines between social media and online shopping. While eCommerce sites also rely on recommendation algorithms, social media is now bridging these two worlds. Millions of users are exposed to new brands and products every day based on their interactions and history of social media scrolling—from following Instagram influencers to watching a video with a sponsored product.