%0 Conference Paper %1 Millecamp_2018 %A Millecamp, Martijn %A Htun, Nyi Nyi %A Jin, Yucheng %A Verbert, Katrien %B Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization %D 2018 %I ACM %K music-recommendation recommender umap2018 user-control %P 101-109 %R 10.1145/3209219.3209223 %T Controlling Spotify Recommendations %U https://doi.org/10.1145%2F3209219.3209223 %X The "black box'' nature of today's recommender systems raises a number of challenges for users, including a lack of trust and limited user control. Providing more user control is interesting to enable end-users to help steer the recommendation process with additional input and feedback. However, different users may have different preferences with regard to such control. To the best of our knowledge, no research has investigated the effect of personal characteristics on visual control techniques in the music recommendation domain. In this paper, we present results of a user study on the web using two different visualisation techniques (a radar chart and sliders) that allows users to control Spotify recommendations. A within-subject design withLatin Square counterbalancing measures was used for the study. Results indicate that the radar chart helped the participants discover a significantly higher number of new songs compared to the sliders. We also found that users' experience with Spotify had an influence on their interaction with different musical attributes. The participants who used Spotify frequently and users with a high individual musical sophistication interacted with the attributes significantly more with the radar chart compared to the sliders. Individual musical sophistication also had a significant impact on their interaction with the interaction techniques. The participants with high musical sophistication interacted significantly more with the radar chart in comparison to the sliders. Based on the feedback from our participants, we provide design suggestions to further improve user control in music recommendation.

@inproceedings{Millecamp_2018, abstract = {The "black box'' nature of today's recommender systems raises a number of challenges for users, including a lack of trust and limited user control. Providing more user control is interesting to enable end-users to help steer the recommendation process with additional input and feedback. However, different users may have different preferences with regard to such control. To the best of our knowledge, no research has investigated the effect of personal characteristics on visual control techniques in the music recommendation domain. In this paper, we present results of a user study on the web using two different visualisation techniques (a radar chart and sliders) that allows users to control Spotify recommendations. A within-subject design withLatin Square counterbalancing measures was used for the study. Results indicate that the radar chart helped the participants discover a significantly higher number of new songs compared to the sliders. We also found that users' experience with Spotify had an influence on their interaction with different musical attributes. The participants who used Spotify frequently and users with a high individual musical sophistication interacted with the attributes significantly more with the radar chart compared to the sliders. Individual musical sophistication also had a significant impact on their interaction with the interaction techniques. The participants with high musical sophistication interacted significantly more with the radar chart in comparison to the sliders. Based on the feedback from our participants, we provide design suggestions to further improve user control in music recommendation. }, added-at = {2022-07-04T18:06:11.000+0200}, author = {Millecamp, Martijn and Htun, Nyi Nyi and Jin, Yucheng and Verbert, Katrien}, biburl = {https://www.bibsonomy.org/bibtex/242e5f188ed5645b226ac7815b5fd3a05/brusilovsky}, booktitle = {Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization}, description = {Controlling Spotify Recommendations | Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization}, doi = {10.1145/3209219.3209223}, interhash = {d242a7fbe446acc1a55ca48409983be5}, intrahash = {42e5f188ed5645b226ac7815b5fd3a05}, keywords = {music-recommendation recommender umap2018 user-control}, month = jul, pages = {101-109}, publisher = {{ACM}}, timestamp = {2022-07-04T18:06:11.000+0200}, title = {Controlling Spotify Recommendations}, url = {https://doi.org/10.1145%2F3209219.3209223}, year = 2018 }