Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A music recommendation system based on music and user grouping
Journal of Intelligent Information Systems - Special issue: Intelligent multimedia applications
Evaluating Interface Variants on Personality Acquisition for Recommender Systems
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Acceptance issues of personality-based recommender systems
Proceedings of the third ACM conference on Recommender systems
The voice of personality: mapping nonverbal vocal behavior into trait attributions
Proceedings of the 2nd international workshop on Social signal processing
Evaluating recommender systems from the user's perspective: survey of the state of the art
User Modeling and User-Adapted Interaction
Don't ask me what i'm like, just watch and listen
Proceedings of the 20th ACM international conference on Multimedia
| Hi-index | 0.00 |
Recommendation Systems involve effort from the user to elicit their preference for the item to be recommended. The contribution of this paper is in eliminating such effort by automatically assessing user's personality and using the personality scores for recommending music tracks to them. Automatic personality assessment is performed by automatically answering a personality questionnaire by observing user's audiovisual recordings. To obtain personality scores, traditionally the answers to the questionnaire are combined using a set of rules specific to the questionnaire to get personality scores. As a second contribution, an approach is proposed to automatically predict personality scores from answers to a questionnaire when the rules to combine the answers may not be known. Promising results on a dataset of 50 movie characters support the proposed approaches.