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fashionvstech · 4 years ago
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AI in your industry (2/6): recommendation engines
How come, in face of so many things to consumer, so many SKUs to buy, you didn’t succumb yet to choice paralysis? One word: hyperpersonalization. Consumers have been able to survive in a world of infinite, chaotic supply, thanks to the bespoke choices brought to them through recommendation engines...
Netflix does it. Amazon does it. Your e-commerce website does it too. And if it doesn’t, you should reconsider why you’re using your current e-commerce platform at all.
I’m talking about personalization of the experience. Consumer are expecting this, as it has permeated their online lives and made it easier to flow from one product/content to the next without feeling completely overwhelmed.
Clearly, if you’ve been watching shows about bank heist (e.g. Casa de Papel), you’re expecting Netflix to suggest something along the same lines, don’t you? It’d be cumbersome if you had to spend time and energy browsing aimlessly at the infinity of movies and shows that didn’t remotely fit your apparent interest. You would actually leave, even.
Recommendation engine, as they are called (“engine” means “system”), are program that filters through the content of a database based on statistics about previous input, choices and behaviors. It turns data about users’ behavior into actionable customization, relevant for conversion.
Content-based filtering are derived from the current user’s actions and preferences — e.g. the user bought several sunglasses, so she gets recommendation for more sunglasses later on. Another exemple is Facebook: the more you click on a content, the more you’ll get similar content...
Maybe you’re starting to see how this can become an issue. While Facebook makes its bread and butter ensuring you’re clicking more of the same stuff (becoming a dubious echo chamber), it’s more noticeable — and annoying — if you get stuck on sunglasses when you’ve had your share of it.
Another technique is collaborative filtering.
Collaborative filtering draws from a dataset of several (hundreds, thousands...) users. Based on their behavior (purchases), you can predict the behavior of other users. The two variants in this approach are based on either users’ similarities (common behavior between users) or items (how users rated similar items). The latter is obviously a good alternative when you did not, or cannot, accumulate data on each users.
Some engines follow a hybrid approach, mixing the two filtering types.
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