%0 Conference Paper %1 Jin_2019 %A Jin, Yucheng %A Htun, Nyi Nyi %A Tintarev, Nava %A Verbert, Katrien %B Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization %D 2019 %I ACM %K interactive-recommender recommender umap2019 user-control %R 10.1145/3320435.3320445 %T ContextPlay: Evaluating User Control for Context-Aware Music Recommendation %U https://doi.org/10.1145%2F3320435.3320445 %X Music preferences are likely to depend on contextual characteristics such as location and activity. However, most recommender systems do not allow users to adapt recommendations to their current context. We therefore built ContextPlay, a context-aware music recommender that enables user control for both contextual characteristics and music preferences. By conducting a mixed-design study (N=114) with four typical scenarios of music listening, we investigate the effect of controlling contextual characteristics in a music recommender system on four aspects: perceived quality, diversity, effectiveness, and cognitive load. Compared to our baseline which only allows to specify music preferences, having additional control for context leads to higher perceived quality and does not increase cognitive load. We also find that the contexts of mood, weather, and location tend to influence user perception of the system. Moreover, we found that users are more likely to modify contexts and their profile during relaxing activities.

@inproceedings{Jin_2019, abstract = {Music preferences are likely to depend on contextual characteristics such as location and activity. However, most recommender systems do not allow users to adapt recommendations to their current context. We therefore built ContextPlay, a context-aware music recommender that enables user control for both contextual characteristics and music preferences. By conducting a mixed-design study (N=114) with four typical scenarios of music listening, we investigate the effect of controlling contextual characteristics in a music recommender system on four aspects: perceived quality, diversity, effectiveness, and cognitive load. Compared to our baseline which only allows to specify music preferences, having additional control for context leads to higher perceived quality and does not increase cognitive load. We also find that the contexts of mood, weather, and location tend to influence user perception of the system. Moreover, we found that users are more likely to modify contexts and their profile during relaxing activities.}, added-at = {2022-11-06T03:35:00.000+0100}, author = {Jin, Yucheng and Htun, Nyi Nyi and Tintarev, Nava and Verbert, Katrien}, biburl = {https://www.bibsonomy.org/bibtex/2ec52eba83fc904f48a259db796332ce6/brusilovsky}, booktitle = {Proceedings of the 27th {ACM} Conference on User Modeling, Adaptation and Personalization}, description = {ContextPlay | Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization}, doi = {10.1145/3320435.3320445}, interhash = {cfd973be17c6735aef71a548ae5315ec}, intrahash = {ec52eba83fc904f48a259db796332ce6}, keywords = {interactive-recommender recommender umap2019 user-control}, month = jun, publisher = {{ACM}}, timestamp = {2022-11-06T03:35:57.000+0100}, title = {{ContextPlay}: Evaluating User Control for Context-Aware Music Recommendation}, url = {https://doi.org/10.1145%2F3320435.3320445}, year = 2019 }