Predicting movies’ eudaimonic and hedonic scores: A machine learning approach using metadata, audio and visual features

Abstract

In the task of modeling user preferences for movie recommender systems, recent research has demonstrated the benefits of describing movies with their eudaimonic and hedonic scores (E and H scores), which reflect the depth of their message and the level of fun experience they provide, respectively. So far, the labeling of movies with their E and H scores has been done manually using a dedicated instrument (a questionnaire), which is time-consuming. To address this issue, we propose an automatic approach for predicting E and H scores. Specifically, we collected E and H scores of 709 movies from 370 users (with a total of 3699 records), augmented this dataset with metadata, audio, and low-level and high-level visual features, and trained machine learning models for predicting the E and H scores of movies. This study investigates the use of machine learning models in predicting the E and H scores of movies using various feature sets, including audio, low-level and high-level visual features, and metadata. We compared the performance of predictive models using different combinations of features with the majority classifier as the baseline approach. The results demonstrate that our proposed machine learning-based models significantly outperform the baseline in predicting E and H scores, particularly when leveraging metadata features. Specifically, the random forest classifier achieved a 20% increase in ROC AUC compared to the baseline when predicting both the E score and the H score. These improvements were found to be statistically significant. Overall, our findings suggest that automated tools for predicting E and H scores in movies are promising alternatives to traditional questionnaire-based approaches.