The Cold Start Problem refers to the challenge of making accurate recommendations or predictions in systems like recommender engines when there is insufficient initial data about users or items. Overcoming this issue often involves leveraging alternative data sources, hybrid approaches, or sophisticated algorithms to infer preferences and patterns despite limited direct information.