  The cold start problem is a common challenge in various domains, including
media use cases such as predicting viewership for newly launched shows on
Over-The-Top (OTT) platforms. In this study, we propose a generic approach to
tackle cold start problems by leveraging metadata and employing multi-model
ensemble techniques. Our methodology includes feature engineering, model
selection, and an ensemble approach based on a weighted average of predictions.
The performance of our proposed method is evaluated using various performance
metrics. Our results indicate that the multi-model ensemble approach
significantly improves prediction accuracy compared to individual models.
