Think about the last time you watched a movie, a video, bought a product online, or saw an advertisement. Whether you used Amazon, YouTube, Netflix, eBay, or saw an ad while browsing any of these sites or others, you have experienced Artificial Intelligence (AI) in the form of recommendation systems.
You might have heard that artificial intelligence (AI) is rising in popularity and is affecting many features of your life—which is true. Artificial intelligence impacts you in ways that you might not know, such as when you browse Netflix or YouTube. However, the list does not stop there. AI is also being used in the medical field, diagnosing millions of people with cancers, predicting and analyzing how personal treatments will result in the health of a person, and even the discovery of drugs.
Artificial intelligence impacts you in ways that you might not know, such as when you browse Netflix or YouTube.
Whenever you use the internet, sites collect cookies, sort of like your user data, to enhance your experience on their site. Let us talk about Netflix. There are attributes to the movies on the site, like the name, category, rating, etc. which can be used to see if you would enjoy the movie. Netflix sees what types of movies you like whenever you watch a movie, and based on that, they can build your user profile and easily recommend you other movies that you might enjoy. For example, if you just watched a comedy movie, Netflix would recommend that you watch another comedy movie. This type of data is called implicit data because you are just interacting with a certain item: a movie or show.
In addition to implicit data, explicit data is given out by the user, through things like reviews, feedback, ratings, etc. This type of data is much harder to get if a user does not like to rate or give off reviews.
Both implicit and explicit data are types of systems called collaborative filtering systems, which make intelligent AI recommendations based on a user’s preference, represented by their data. Another type of recommendation system is called content-based recommendation, which is literally what the name implies. For example, a job-searching site can use a user’s description, like data scientist, to find jobs that might fit them. Thus, it is obvious that content-based recommendation systems are much faster and can easily make correct predictions. However, there are many reasons why content-based recommendation systems fall inferior to collaborate filtering systems. For example, more complex sites or usages where it’s harder to recommend a user items just based on their description needs AI interference in order to create an enjoyable experience for users that ensures that they will come back to the website.
As you have read, there are multiple factors that play into how your items get recommended to you, whether it is fully handled by artificial intelligence or semi-handled by it. Hopefully, this article helped you understand this important factor that companies rely on, which you may have not realized. Please read our other articles on artificial intelligence!