Bad recommendations, good algorithm

If you’ve ever shopped online (*cough* Amazon *cough*), you’ve probably experienced the “vacuum cleaner effect”. You carefully buy one expensive item (e.g. a vacuum cleaner) and then you receive dozens of recommendations for other vacuum cleaners to buy: by email, everywhere on the retailer’s website, or sometimes in the ads you see on other websites.

In other terms, Amazon is a 1 trillion dollar company that employs hundreds of data scientists and is incapable of understanding that if you bought an expensive appliance, buying another one of the same category in the next weeks is what you’re *least* likely to do!

But let’s think about the problem for a second. Suggesting item that are similar to what you just bought is actually the core feature of recommendation algorithms! Detecting that it might be inappropriate for some precise categories of items is not an easy task! It would require some careful analysis of the performance by categories, which would be prone to many potential errors: sampling variance, categorization error (maybe some manual tagging would be required), temporal fluctuations, etc.
So fixing this little annoyance for the consumer might take a few weeks of research, a couple months of integration, and still fail in some cases. It could end up costing several hundred thousands of dollars to fix this, not even counting that it could also affect the performance of the global recommendation algorithm.

So the recommendations might be bad, but in the end the algorithm is valuable nonetheless. Remember, machine learning and artificial intelligence are pretty stupid, but they are very valuable!


Recommender systems are awesome! Very excited to say I’ll be at RecSys 2018 in Vancouver next month to learn more about them 🙂


— Featured image: View of downtown Vancouver from the Lookout Tower at Harbour Centre, by Magnus Larsson.