It appears to any end user that knows some basic logic and looping concepts that the advanced AI/ML/whatever recommendation engine is no better than looping ove

It appears to any end user that knows some basic logic and looping concepts that the advanced AI/ML/whatever recommendation engine is no better than looping over the list of recent content (videos, products, etc.) and selecting more content that has same keywords. If you remove the volume and distributed systems elements for large systems its probably a single SQL query joining 2 tables, history and products (or videos or whatever). For the bike rack example, to make it "smarter" you could have a product type that is something like "one-time purchase" and have a linked product type that is accessories for the one-time product, like helmets, pumps, tire changing kits, inner tubes, water bottles, etc. This would be what a retail worker at a bike shop would do - ask if you need any accessories, maybe point out clearance/sale items. Thats it, no fancy engines/AI/ML needed. If the customer wants other stuff, there is a search bar they will likely use. I chalk it up to Resume Driven Development and just the innate tendency of engineers over-engineering things because that's how our brains work.