Happy New Year, everyone,

Welcome to January, the month where most behavioural-based e-commerce recommender systems struggle.

Hopefully, December was full of lots of transactions, so the system has a lot of good-quality data. But most customers don’t have or want to have the same shopping habits in January as they did in December. Also, what makes it all murkier is that most people buy presents for other people, not just one person but several people, so most taste profiles have gone crazy.

Your recommender training pipeline might still produce offline evaluations on par with last month’s evaluations (at least if the data still includes Christmas shoppers), but will it correspond with what will happen online? Andrew Ng talks about the importance of your data representing the production environment in which it should be deployed. The question then begs: Is it better to remove the December data altogether, as November data might look much more like what is happening in January? 

No matter what, it’s now a good idea to filter out anything Christmas-related.