Using Affective Features from Media Content Metadata for Better Movie Recommendations

John Kalung Leung, Igor Griva, William G. Kennedy

2020

Abstract

This paper investigates the causality in the decision making of movie recommendations through the users' affective profiles. We advocate a method of assigning emotional tags to a movie by the auto-detection of the affective features in the movie's overview. We apply a text-based Emotion Detection and Recognition model, which trained by tweets short messages and transfers the learned model to detect movie overviews’ implicit affective features. We vectorize the affective movie tags to represent the mood embeddings of the movie. We obtain the user's emotional features by taking the average of all the movies' affective vectors the user has watched. We apply five-distance metrics to rank the Top-N movie recommendations against the user's emotion profile. We found Cosine Similarity distance metrics performed better than other distance metrics measures. We conclude that by replacing the top-N recommendations generated by the Recommender with the reranked recommendations list made by the Cosine Similarity distance metrics, the user will effectively get affective aware top-N recommendations while making the Recommender feels like an Emotion Aware Recommender.

Paper Citation

in Harvard Style

Leung J., Griva I. and Kennedy W. (2020). Using Affective Features from Media Content Metadata for Better Movie Recommendations. In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 1: KDIR; ISBN 978-989-758-474-9, SciTePress, pages 161-168. DOI: 10.5220/0010056201610168

in Bibtex Style

@conference{kdir20,
author={John Kalung Leung and Igor Griva and William G. Kennedy},
title={Using Affective Features from Media Content Metadata for Better Movie Recommendations},
booktitle={Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 1: KDIR},
year={2020},
pages={161-168},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010056201610168},
isbn={978-989-758-474-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 1: KDIR
TI - Using Affective Features from Media Content Metadata for Better Movie Recommendations
SN - 978-989-758-474-9
AU - Leung J.
AU - Griva I.
AU - Kennedy W.
PY - 2020
SP - 161
EP - 168
DO - 10.5220/0010056201610168
PB - SciTePress