Unraveling Cold Start Enigmas in Predictive Analytics for OTT Media: Synergistic Meta-Insights and Multimodal Ensemble Mastery
This addresses a domain-specific challenge for OTT media platforms, but it is incremental as it builds on existing ensemble methods.
The study tackled the cold start problem in predicting viewership for new shows on OTT platforms by using metadata and multi-model ensemble techniques, resulting in significantly improved prediction accuracy compared to individual models.
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.