Why some recommendations fall flat: Recommendation engines & their challenges

Using algorithms to make purchasing suggestions is big business. Netflix reported that its recommendation engine contributes $1 billion to its bottom line every year. However, sometimes the suggestions are way off.

Take, for example, an ad I received to apply for a job as a van driver. I have never been a professional driver, I don’t even like driving and I have never owned a van. It’s clear that this recommendation engine knows nothing about me.

There are several different ways recommendation algorithms can reach the wrong conclusions. Here are just a few examples for each type of recommendation engine.