We have become increasingly reliant on recommender systems to help us make decisions in our daily live. As such, it is becoming essential to explain to users how these systems reason to enable them to correct system assumptions and to trust the system. The advantages of explaining the recommendation process has been shown by a vast amount of research. Additionally, previous studies showed that personality affects users' attitudes, tastes and information processing. However, it is still unclear whether personality has an impact on the way users process and perceive explanations. In this paper, we report the results of a study that investigated differences between personal characteristics of the perception and the gaze pattern of a music recommender interface in the presence and absence of explanations. We investigated the differences between Need For Cognition, Musical Sophistication and the Big Five personality traits. Results show empirical evidence of the differences between Musical Sophistication and Openness on both perception and gaze pattern. We found that users with a high Musical Sophistication and a low Openness score benefit the most from explanations.
Description
What's in a User? Towards Personalising Transparency for Music Recommender Interfaces | Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization
%0 Conference Paper
%1 Millecamp_2020
%A Millecamp, Martijn
%A Htun, Nyi Nyi
%A Conati, Cristina
%A Verbert, Katrien
%B Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization
%D 2020
%I ACM
%K information-visualization recommender transparency umap2020 user-control
%R 10.1145/3340631.3394844
%T What's in a User? Towards Personalising Transparency for Music Recommender Interfaces
%U https://doi.org/10.1145%2F3340631.3394844
%X We have become increasingly reliant on recommender systems to help us make decisions in our daily live. As such, it is becoming essential to explain to users how these systems reason to enable them to correct system assumptions and to trust the system. The advantages of explaining the recommendation process has been shown by a vast amount of research. Additionally, previous studies showed that personality affects users' attitudes, tastes and information processing. However, it is still unclear whether personality has an impact on the way users process and perceive explanations. In this paper, we report the results of a study that investigated differences between personal characteristics of the perception and the gaze pattern of a music recommender interface in the presence and absence of explanations. We investigated the differences between Need For Cognition, Musical Sophistication and the Big Five personality traits. Results show empirical evidence of the differences between Musical Sophistication and Openness on both perception and gaze pattern. We found that users with a high Musical Sophistication and a low Openness score benefit the most from explanations.
@inproceedings{Millecamp_2020,
abstract = {We have become increasingly reliant on recommender systems to help us make decisions in our daily live. As such, it is becoming essential to explain to users how these systems reason to enable them to correct system assumptions and to trust the system. The advantages of explaining the recommendation process has been shown by a vast amount of research. Additionally, previous studies showed that personality affects users' attitudes, tastes and information processing. However, it is still unclear whether personality has an impact on the way users process and perceive explanations. In this paper, we report the results of a study that investigated differences between personal characteristics of the perception and the gaze pattern of a music recommender interface in the presence and absence of explanations. We investigated the differences between Need For Cognition, Musical Sophistication and the Big Five personality traits. Results show empirical evidence of the differences between Musical Sophistication and Openness on both perception and gaze pattern. We found that users with a high Musical Sophistication and a low Openness score benefit the most from explanations.
},
added-at = {2020-07-15T19:00:45.000+0200},
author = {Millecamp, Martijn and Htun, Nyi Nyi and Conati, Cristina and Verbert, Katrien},
biburl = {https://www.bibsonomy.org/bibtex/29af6081d62492eab3080f8ff93f81ccd/brusilovsky},
booktitle = {Proceedings of the 28th {ACM} Conference on User Modeling, Adaptation and Personalization},
description = {What's in a User? Towards Personalising Transparency for Music Recommender Interfaces | Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization},
doi = {10.1145/3340631.3394844},
interhash = {1ee3f857a8dd36a52b95fa5ceabb2cd3},
intrahash = {9af6081d62492eab3080f8ff93f81ccd},
keywords = {information-visualization recommender transparency umap2020 user-control},
month = jul,
publisher = {{ACM}},
timestamp = {2020-09-02T17:15:52.000+0200},
title = {What's in a User? Towards Personalising Transparency for Music Recommender Interfaces},
url = {https://doi.org/10.1145%2F3340631.3394844},
year = 2020
}