Learning Personalised Models for Automatic Self-Reported Personality Recognition

Hanan Salam, Viswonathan Manoranjan, Jian Jiang, Oya Celiktutan
Understanding Social Behavior in Dyadic and Small Group Interactions, PMLR 173:53-73, 2022.

Abstract

Previous research has revealed differences in personality traits among different genders, age groups, and even cultures. However, existing methods have focused on one-fits-all approaches only and performed personality recognition without taking into consideration the user’s profiles. In this paper, we propose to learn personalised models of self-reported big five personality traits. Our proposed approach automatically learns deep learning architectures for different user profiles using Neural Architecture Search (NAS) for predicting the Big Five personality traits from multimodal behavioural features. We experiment with two different user profiling criteria, namely, gender and age, and compare the results of our approach with the state-of-the-art methods. Overall, our results show that personalised models improve the performance as compared to the generic model. Particularly, gender-based user profiling combined with bimodal features reduces the prediction error by 0.128, achieving the state-of-the-art performance on the UDIVA dataset.

Cite this Paper

BibTeX
@InProceedings{pmlr-v173-salam22a, title = {Learning Personalised Models for Automatic Self-Reported Personality Recognition}, author = {Salam, Hanan and Manoranjan, Viswonathan and Jiang, Jian and Celiktutan, Oya}, booktitle = {Understanding Social Behavior in Dyadic and Small Group Interactions}, pages = {53--73}, year = {2022}, editor = {Palmero, Cristina and Jacques Junior, Julio C. S. and Clapés, Albert and Guyon, Isabelle and Tu, Wei-Wei and Moeslund, Thomas B. and Escalera, Sergio}, volume = {173}, series = {Proceedings of Machine Learning Research}, month = {16 Oct}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v173/salam22a/salam22a.pdf}, url = {https://proceedings.mlr.press/v173/salam22a.html}, abstract = {Previous research has revealed differences in personality traits among different genders, age groups, and even cultures. However, existing methods have focused on one-fits-all approaches only and performed personality recognition without taking into consideration the user’s profiles. In this paper, we propose to learn personalised models of self-reported big five personality traits. Our proposed approach automatically learns deep learning architectures for different user profiles using Neural Architecture Search (NAS) for predicting the Big Five personality traits from multimodal behavioural features. We experiment with two different user profiling criteria, namely, gender and age, and compare the results of our approach with the state-of-the-art methods. Overall, our results show that personalised models improve the performance as compared to the generic model. Particularly, gender-based user profiling combined with bimodal features reduces the prediction error by 0.128, achieving the state-of-the-art performance on the UDIVA dataset.} }
Endnote
%0 Conference Paper %T Learning Personalised Models for Automatic Self-Reported Personality Recognition %A Hanan Salam %A Viswonathan Manoranjan %A Jian Jiang %A Oya Celiktutan %B Understanding Social Behavior in Dyadic and Small Group Interactions %C Proceedings of Machine Learning Research %D 2022 %E Cristina Palmero %E Julio C. S. Jacques Junior %E Albert Clapés %E Isabelle Guyon %E Wei-Wei Tu %E Thomas B. Moeslund %E Sergio Escalera %F pmlr-v173-salam22a %I PMLR %P 53--73 %U https://proceedings.mlr.press/v173/salam22a.html %V 173 %X Previous research has revealed differences in personality traits among different genders, age groups, and even cultures. However, existing methods have focused on one-fits-all approaches only and performed personality recognition without taking into consideration the user’s profiles. In this paper, we propose to learn personalised models of self-reported big five personality traits. Our proposed approach automatically learns deep learning architectures for different user profiles using Neural Architecture Search (NAS) for predicting the Big Five personality traits from multimodal behavioural features. We experiment with two different user profiling criteria, namely, gender and age, and compare the results of our approach with the state-of-the-art methods. Overall, our results show that personalised models improve the performance as compared to the generic model. Particularly, gender-based user profiling combined with bimodal features reduces the prediction error by 0.128, achieving the state-of-the-art performance on the UDIVA dataset.
APA
Salam, H., Manoranjan, V., Jiang, J. & Celiktutan, O.. (2022). Learning Personalised Models for Automatic Self-Reported Personality Recognition. Understanding Social Behavior in Dyadic and Small Group Interactions, in Proceedings of Machine Learning Research 173:53-73 Available from https://proceedings.mlr.press/v173/salam22a.html.

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