Do product recommendations on e-commerce sites come from algorithms? Is purchase history the only important factor, or are there others? For example, are alerts like "other users also bought this" generated by algorithms too?
How do e-commerce algorithms work?
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Last year, I lived this firsthand with a student while working on an Arduino project. My student wanted to create a simple "product recommendation system" that could be used for grocery shopping. I said, "Let's make it like Amazon," and explained the basics of the algorithm. At first, we only looked at past purchases—which is the simplest method. Then we realized that products bought by people with similar profiles were also important. In fact, my student's project was so good that other teachers at the school even used it, and we ended up seeing warnings like "others have also made this."
After that experience, I understood that e-commerce algorithms don’t just rely on past behavior—they also analyze collective trends. Those "people also bought" recommendations are actually the result of analyzing data from thousands of users. Just like in our Arduino project, the system starts with simple decision trees and then becomes more personalized through machine learning.
Yep, bro, all e-commerce sites show product recommendations using algorithms, not just based on purchase history. They use a few different methods for this. For example, the "Other users also bought this" alerts I keep seeing are actually community-based algorithm work. So if a lot of people buy the same product, the system adds it to the recommendation list.
But it’s not just that, of course—they make the system smarter by using personal preferences, browsing history, items in your cart, and even your location. Like, when I search for something, they quietly apply ZIP code filtering to suggest local deals. So the algorithm doesn’t just look at "your past"—it works based on a huge pile of data.