You go to the local supermarket and you buy a pack of noodles, a crate of eggs, some spicy Cameroon pepper, and 2 kilos of frozen chicken. Then you proceed to check out your purchases. From everything you bought, it can be safely assumed that you’re trying to beat the Indomie adverts at their own game. But there’s one key thing missing, so the cashier says. ‘Sir/Madam, won’t you buy some fresh groundnut oil? We have several brands in stock’. He/she then goes ahead to list some of them and maybe their prices. You may or may not buy one eventually.
Nevertheless, he made a fairly accurate suggestion or recommendation of something you ought to be interested in, based on your purchases. In the simplest of terms, this is what a recommendation engine does.
Recommendation Engines are a popular form of Artificial Intelligence that has found many commercial applications. It’s behind the movie recommendations that you get on Netflix. Netflix has said that its proprietary recommendation engine is worth $1 billion. Imagine that. A bunch of complex code in a computer somewhere is worth five times the dividends the FG hopes to earn from the NLNG this year.
A typical recommendation engine works using A to B, or input to output mappings. That is, it takes information, in the form of data about you and other users, which is processed to output recommendations that are based on the input. So, if you buy a smartphone online and 100 previous users bought that smartphone and a headset, a recommendation engine would suggest that headset for you.
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This data comes in several forms. When you browse any webpage, you leave a digital footprint that is matched to your IP address and can be stored to create a unique profile of you. This is implicit data. When you subscribe to a channel, post a comment, rate an app on your app store, or put items in your online shopping cart (with no intention of actually checking them out), you’re providing explicit data about your preferences.
The combination of implicit and explicit data is used by algorithms to create a composite profile of you. We all have profiles on the online platforms that we frequently use. The more you use them, the smarter the recommendation engine gets about your preferences. It’s like the shop clerk in the beginning. Maybe the first time, you told him that you weren’t interested in cooking oil because you had a stockpile at home. Give that same reply when you visit the store a second time, he’s not likely to ask you the third time.
Recommendation engines are in essence a bet on human psychology. We are creatures of habit, who tend to repeat previous choices. 1 in 3 people who purchase something on Amazon does so based on a recommendation. That’s $93 billion from just suggesting the next best thing. If you’re a business owner, a recommendation engine is a tool you should set your eyes on.
BIO & CONTACT DETAILS
Akin Agunbiade studied Law at Obafemi Awolowo University and is currently enrolled at the Nigerian Law School. He is the producer of the Fit and Proper Podcast, a show dedicated to the Bar Finals of the Nigerian Law School. He is an avid researcher on Artificial Intelligence & Law, with several articles, papers, and a published book, titled, ‘Artificial Intelligence & Law: A Nigerian Perspective’. He is passionate about democratizing access to legal services and legal education. He can be reached via akinifeanyi5@gmail.com and on Twitter @Akin_Agunbiade
