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How to solve the “cold start problem” in an ML recommendation system
How to solve the “cold start problem” in an ML recommendation system

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 encountered this problem when launching an ML-powered product recommendation system for an online shopping platform a few years ago. Before introducing machine learning, the platform simply recommended the most popular products. The new system was designed to provide personalized recommendations.

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