MovieOcean: Assessment of a Personality-based Recommender System

Luca Rolshoven, Corina Masanti, Jhonny Pincay, Jhonny Pincay, Luis Terán, José Mancera, Edy Portmann

2022

Abstract

This research effort explores the incorporation of personality treats into user-user collaborative filtering algorithms. To explore the performance of such a method, MovieOcean, a movie recommender system that uses a questionnaire based on the Big Five model to generate personality profiles, was implemented. These personality profiles are used to precompute personality-based neighborhoods, which are then used to predict movie ratings and generate recommendations. In an offline analysis, the root mean square error metric is computed to analyze the accuracy of the predicted ratings and the F1-score to assess the relevance of the recommendations for the personality-based and a standard-rating-based approach. The obtained results showed that the root mean square error of the personality-based recommender system improves when the personality has a higher weight than the information about the user ratings. A subsequent t-test was conducted for the proposed personality-based approach underperformed based on the root mean square error metric. Furthermore, interviews with users suggested that including aspects of personality when computing recommendations is well-perceived and can indeed help improve current recommendation methods.

Paper Citation

in Harvard Style

Rolshoven L., Masanti C., Pincay J., Terán L., Mancera J. and Portmann E. (2022). MovieOcean: Assessment of a Personality-based Recommender System. In Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-569-2, pages 690-698. DOI: 10.5220/0011002500003179

in Bibtex Style

@conference{iceis22,
author={Luca Rolshoven and Corina Masanti and Jhonny Pincay and Luis Terán and José Mancera and Edy Portmann},
title={MovieOcean: Assessment of a Personality-based Recommender System},
booktitle={Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2022},
pages={690-698},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011002500003179},
isbn={978-989-758-569-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - MovieOcean: Assessment of a Personality-based Recommender System
SN - 978-989-758-569-2
AU - Rolshoven L.
AU - Masanti C.
AU - Pincay J.
AU - Terán L.
AU - Mancera J.
AU - Portmann E.
PY - 2022
SP - 690
EP - 698
DO - 10.5220/0011002500003179