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Temporal modelling for movie success prediction: a comparative study on the applicability of different deep learning models in entertainment analytics
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Resumo(s)
This thesis investigates factors influencing movie success using deep learning techniques. It
compares weak supervision to supervised deep learning approaches when predicting movie success
after the box office release. A key focus of this study is to establish whether the multiple instance
learning (MIL) approach can perform more accurate predictions using multivariate time series data
from the post-release stage of movies. Additionally, it is assessed whether a MIL-based architecture
delivers more interpretable results compared to supervised architectures. By integrating diverse
data sources and features, this study provides a comprehensive perspective on how different deep
learning techniques can be used to measure audience engagement as a time series to classify box
office success. The findings advance academic understanding of the applicability of MIL in the
movie domain and offer insights for industry stakeholders aiming to enhance deep learning
architecture selection.
Descrição
Palavras-chave
Multivariate Time Series Classification Movie success prediction Model comparison Multiple instance learning