Ideas for Spotify Customization: Enhancing the User’s Experience
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-06417-3_3
Reference4 articles.
1. Mogharrab, A., Odom, W.: Ecphoria player: exploring, revisiting, and living-with a lifetime of digital music. In: Proceedings of the 2018 ACM Conference Companion Publication on Designing Interactive Systems (DIS 2018 Companion), pp. 189–194. Association for Computing Machinery, New York, NY, USA (2018). https://doi-org.proxyiub.uits.iu.edu/10.1145/3197391.3205434
2. Melchiorre, A.B., Zangerle, E., Schedl, M.: Personality bias of music recommendation algorithms. In: Fourteenth ACM Conference on Recommender Systems, pp. 533–538. Association for Computing Machinery, New York, NY, USA (2020). https://doi-org.proxyiub.uits.iu.edu/10.1145/3383313.3412223
3. Ferwerda, B., Yang, E., Schedl, M., Tkalcic, M.: Personality traits predict music taxonomy preferences. In: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA 2015), pp. 2241–2246. Association for Computing Machinery, New York, NY, USA (2015). https://doi-org.proxyiub.uits.iu.edu/10.1145/2702613.2732754
4. Jannach, D., Jesse, M., Jugovac, M., Trattner, C.: Exploring multi-list user interfaces for similar-item recommendations. In: Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, pp. 224–228. Association for Computing Machinery, New York, NY, USA (2021). https://doi-org.proxyiub.uits.iu.edu/10.1145/3450613.3456809
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