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

Unraveling Cold Start Enigmas in Predictive Analytics for OTT Media: Synergistic Meta-Insights and Multimodal Ensemble Mastery