You probably recently visited the good-old For You page on TikTok. Or clicked in one of the recommended videos on your right side on YouTube. Or listened to a Daily Mix playlist Spotify made for you. Or saw Amazon’s “People like you also bought:”. You get the idea; everyday you are around the results of various recommender systems.
You might have already figured it out, but these platforms are always collecting a lot of information about you. We like to call that data.
Data is what feeds the recommender systems, and makes their recommendations so accurate. To unpack the black box between the data and the suspiciously-accurate recommendations, we will need to inevitably talk about two buzzwords that have been thrown around a lot in recent years: data science and AI.
Whether you are excited or tired of hearing about them, these fields have been gaining a lot of attention lately, and for good reason - they're incredibly useful in today's world. In fact, recommender systems, are quite the blend between AI and Data Science, which is why we need to know the very basic benefits of these fields before we dive deeper.
Data is everywhere, from what you watch, to what you buy, to whom you talk. Data is being generated at vast amounts every moment, especially after the rise of the internet. But collecting all of this data is useless if we don't know what to do with it. That's where data science comes in - it allows us to translate all of this data into knowledge to make better decisions. To break it down, recommender systems collect plenty of data about users' behaviors. Then, they process all that information, and “learn” from it. Then, they come up with personalized recommendations. The “learning” is where AI comes in.
Artificial Intelligence deals with creating intelligent machines that can perform tasks that typically require human intelligence. These tasks can include anything from recognizing images and speech to playing games and... you guessed it - making recommendations.
Both AI and Data Science are changing the world as we know it, because they’re able to process massive amounts of information at once, which allows them to automate tasks that humans can’t; from detecting fraud, to expecting customers’ needs before they even have them.
