首页|Findings from University of Primorska in the Area of Machine Learning Reported (Predicting Movies' Eudaimonic and Hedonic Scores: a Machine Learning Approach Using Metadata, Audio and Visual Features)
Findings from University of Primorska in the Area of Machine Learning Reported (Predicting Movies' Eudaimonic and Hedonic Scores: a Machine Learning Approach Using Metadata, Audio and Visual Features)
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NETL
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Elsevier
A new study on Machine Learning is now available. According to news reporting from Koper, Slovenia, by NewsRx journalists, research stated, “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.” Financial support for this research came from CogniCom grant - University of Primorska. The news correspondents obtained a quote from the research from the University of Primorska, “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.”
KoperSloveniaEuropeCyborgsEmerging TechnologiesMachine LearningUniversity of Primorska
NETL
NSTL
Elsevier
