Authors:
Luca Rolshoven
1
;
Corina Masanti
1
;
Jhonny Pincay
2
;
3
;
Luis Terán
2
;
José Mancera
2
and
Edy Portmann
2
Affiliations:
1
University of Bern, Hochschulstrasse 6, Bern, Switzerland
;
2
Human-IST Institute, University of Fribourg, Boulevard de Pérolles 90, Fribourg, Switzerland
;
3
Pontificia Universidad Católica del Ecuador, Av. 12 de Octubre 1076, Quito, Ecuador
Keyword(s):
Recommender Systems, Personality-based Recommenders, Personalized Recommendations, Big Five Model.
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 underp
erformed 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.
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