How to solve the “cold start problem” in an ML recommendation system (opens in new tab)

One common problem teams face when deploying machine learning products is the cold start problem, where a shortage of quality data limits the performance and value an ML system can deliver. This is especially visible in recommendation systems: when there isn’t enough information about new users or new items, the model tends to underperform. A team we closely worked with […] The post How to solve the “cold start problem” in an ML recommendation system appeared first on GoPractice.