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

• URL: http://arxiv.org/abs/2305.08120v1
• Date: Sun, 14 May 2023 10:46:20 GMT
• Title: Unraveling Cold Start Enigmas in Predictive Analytics for OTT Media: Synergistic Meta-Insights and Multimodal Ensemble Mastery
• Authors: K. Ganguly, A. Patra
• Abstract summary: We propose a generic approach to tackle cold start problems by leveraging metadata and employing multi-model ensemble techniques. Our results indicate that the multi-model ensemble approach significantly improves prediction accuracy compared to individual models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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