From User Context to Tailored Playlists: A User Centered Approach to Improve Music Recommendation System
Resumo
Systems for recommending music have become a popular tool for delivering track suggestions to users that align with their unique listening habits or preferences. Despite their ubiquity, a recurring focus in these systems has been the accuracy of predictions, often bypassing the impact of user experience (UX) while generating recommendations. The so-called cold-start problem, where the system encounters new users without sufficient data about them, has remained a persistent challenge. This research introduces an approach based on user experience and other aspects that may influence music recommendation (e.g., the cold-start problem, feedback, cognitive effort). The approach, designed for integration into a mobile app, emphasizes context by pre-analyzing user-created playlists for their current context. Our investigation employed the Intermediate Semiotic Inspection Method (ISIM) to evaluate the system's communicability. This method allowed us to spotlight three categories essential to music recommendation systems: innovative suggestions, constant updates, and users' engagement in rating. We also apply the Technology Acceptance Model (TAM) to evaluate the system's acceptance. Our work showcases how a semiotic inspection approach, combined with a focus on user context and feedback, can drastically enhance user experience in music recommendation systems.
Palavras-chave:
music recommendation, user experience, context, evaluation
Referências
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Willian G Assuncao, Lara SG Piccolo, and Luciana AM Zaina. 2022. Considering emotions and contextual factors in music recommendation: a systematic literature review. Multimedia Tools and Applications (2022), 1–41.
Willian Garcias Assunção, Raquel Oliveira Prates, and Luciana Aparecida Martinez Zaina. 2023. Managing Cold-Start Issues in Music Recommendation Systems: An Approach Based on User Experience. In Companion Proceedings of the 2023 ACM SIGCHI Symposium on Engineering Interactive Computing Systems. 31–37.
Jia-Wei Chang, Ching-Yi Chiou, Jia-Yi Liao, Ying-Kai Hung, Chien-Che Huang, Kuan-Cheng Lin, and Ying-Hung Pu. 2019. Music recommender using deep embedding-based features and behavior-based reinforcement learning. Multimedia Tools and Applications (2019), 1–28.
Ching-Wei Chen, Paul Lamere, Markus Schedl, and Hamed Zamani. 2018. Recsys Challenge 2018: Automatic Music Playlist Continuation. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys ’18). Association for Computing Machinery, New York, NY, USA, 527–528. DOI: 10.1145/3240323.3240342
Fred D Davis. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly (1989), 319–340.
Erica Rodrigues de Oliveira and Raquel Oliveira Prates. 2018. Intermediated semiotic inspection method. In Proceedings of the 17th Brazilian Symposium on Human Factors in Computing Systems. 1–10.
Clarisse Sieckenius De Souza. 2005. The semiotic engineering of human-computer interaction. MIT press.
Clarisse Sieckenius de Souza, Carla Faria Leitão, Raquel Oliveira Prates, Sílvia Amélia Bim, and Elton José da Silva. 2010. Can inspection methods generate valid new knowledge in HCI? The case of semiotic inspection. International Journal of Human-Computer Studies 68, 1-2 (2010), 22–40.
Clarisse Sieckenius De Souza, Carla Faria Leitão, Raquel Oliveira Prates, and Elton José Da Silva. 2006. The semiotic inspection method. In Proceedings of VII Brazilian symposium on Human factors in computing systems. 148–157.
Anind K Dey. 2001. Understanding and using context. Personal and ubiquitous computing 5 (2001), 4–7.
Jean Garcia-Gathright, Brian St. Thomas, Christine Hosey, Zahra Nazari, and Fernando Diaz. 2018. Understanding and evaluating user satisfaction with music discovery. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 55–64.
Ron Garland. 1991. The mid-point on a rating scale: Is it desirable. Marketing bulletin 2, 1 (1991), 66–70.
G Geetha, M Safa, C Fancy, and D Saranya. 2018. A hybrid approach using collaborative filtering and content based filtering for recommender system. In Journal of Physics: Conference Series. IOP Publishing.
Michael Gillhofer and Markus Schedl. 2015. Iron maiden while jogging, debussy for dinner? An analysis of music listening behavior in context. In MultiMedia Modeling: 21st International Conference, MMM 2015, Sydney, NSW, Australia, January 5-7, 2015, Proceedings, Part II 21. Springer, 380–391.
Yike Guo, Chao Wu, and Diego Peteiro-Barral. 2012. An EEG-Based brain informatics application for enhancing music experience. In International Conference on Brain Informatics. Springer, 265–276.
Marc Hassenzahl. 2010. Experience design: Technology for all the right reasons. Synthesis lectures on human-centered informatics 3, 1 (2010), 1–95.
Karim M Ibrahim, Jimena Royo-Letelier, Elena V Epure, Geoffroy Peeters, and Gael Richard. 2020. Audio-based auto-tagging with contextual tags for music. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 16–20.
Elliot Jenkins and Yanyan Yang. 2016. Creating a music recommendation and streaming application for android. In International Conference on Database and Expert Systems Applications. Springer, 201–215.
Chengkun Jiang and Yuan He. 2016. Smart-dj: Context-aware personalization for music recommendation on smartphones. In 2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 133–140.
Yucheng Jin, Nyi Nyi Htun, Nava Tintarev, and Katrien Verbert. 2019. ContextPlay: Evaluating User Control for Context-Aware Music Recommendation. In Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization. 294–302.
Yucheng Jin, Nava Tintarev, Nyi Nyi Htun, and Katrien Verbert. 2020. Effects of personal characteristics in control-oriented user interfaces for music recommender systems. User Modeling and User-Adapted Interaction 30 (2020), 199–249.
Robert Johns. 2005. One size doesn’t fit all: Selecting response scales for attitude items. Journal of Elections, Public Opinion & Parties 15, 2 (2005), 237–264.
Dehui Luo, Xiang Wan, Jiming Liu, and Tiejun Tong. 2018. Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range. Statistical methods in medical research 27, 6 (2018), 1785–1805.
Maake Benard Magara, Sunday Ojo, Seleman Ngwira, and Tranos Zuva. 2016. MPlist: Context aware music playlist. In 2016 IEEE International Conference on Emerging Technologies and Innovative Business Practices for the Transformation of Societies (EmergiTech). IEEE, 309–316.
Erica R Oliveira, Luiz CS Luz, and Raquel O Prates. 2008. Aplicação semi-estruturada do método de inspeção semiótica: estudo de caso para o domínio educacional. In Proceedings of the VIII Brazilian Symposium on Human Factors in Computing Systems. 50–59.
Rohaila Mohd Rosly and Fariza Khalid. 2018. Evaluation of the “e-Daftar” system using the Technology Acceptance Model (TAM). Creative Education 9, 5 (2018), 675–686.
Elena Shakirova. 2017. Collaborative filtering for music recommender system. In 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). IEEE, 548–550.
Tiancheng Shen, Jia Jia, Yan Li, Yihui Ma, Yaohua Bu, Hanjie Wang, Bo Chen, Tat-Seng Chua, and Wendy Hall. 2020. Peia: Personality and emotion integrated attentive model for music recommendation on social media platforms. In Proceedings of the AAAI conference on artificial intelligence, Vol. 34. 206–213.
Keith S Taber. 2018. The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in science education 48 (2018), 1273–1296.
Gabriel Viana Teixeira and Luciana AM Zaina. 2022. Using Lean Personas to the Description of UX-related Requirements: A Study with Software Startup Professionals.. In ICEIS (2). 211–222.
Viswanath Venkatesh and Hillol Bala. 2008. Technology acceptance model 3 and a research agenda on interventions. Decision sciences 39, 2 (2008), 273–315.
Viswanath Venkatesh and Fred D Davis. 2000. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science 46, 2 (2000), 186–204.
Viswanath Venkatesh, Michael G Morris, Gordon B Davis, and Fred D Davis. 2003. User acceptance of information technology: Toward a unified view. MIS quarterly (2003), 425–478.
Xinxi Wang, David Rosenblum, and Ye Wang. 2012. Context-aware mobile music recommendation for daily activities. In Proceedings of the 20th ACM international conference on Multimedia. 99–108.
Eva Zangerle and Christine Bauer. 2022. Evaluating Recommender Systems: Survey and Framework. Comput. Surveys 55, 8 (2022), 1–38.
Qian Zhao. 2018. User-Centric Design and Evaluation of Online Interactive Recommender Systems. (2018).
Willian G Assuncao, Lara SG Piccolo, and Luciana AM Zaina. 2022. Considering emotions and contextual factors in music recommendation: a systematic literature review. Multimedia Tools and Applications (2022), 1–41.
Willian Garcias Assunção, Raquel Oliveira Prates, and Luciana Aparecida Martinez Zaina. 2023. Managing Cold-Start Issues in Music Recommendation Systems: An Approach Based on User Experience. In Companion Proceedings of the 2023 ACM SIGCHI Symposium on Engineering Interactive Computing Systems. 31–37.
Jia-Wei Chang, Ching-Yi Chiou, Jia-Yi Liao, Ying-Kai Hung, Chien-Che Huang, Kuan-Cheng Lin, and Ying-Hung Pu. 2019. Music recommender using deep embedding-based features and behavior-based reinforcement learning. Multimedia Tools and Applications (2019), 1–28.
Ching-Wei Chen, Paul Lamere, Markus Schedl, and Hamed Zamani. 2018. Recsys Challenge 2018: Automatic Music Playlist Continuation. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys ’18). Association for Computing Machinery, New York, NY, USA, 527–528. DOI: 10.1145/3240323.3240342
Fred D Davis. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly (1989), 319–340.
Erica Rodrigues de Oliveira and Raquel Oliveira Prates. 2018. Intermediated semiotic inspection method. In Proceedings of the 17th Brazilian Symposium on Human Factors in Computing Systems. 1–10.
Clarisse Sieckenius De Souza. 2005. The semiotic engineering of human-computer interaction. MIT press.
Clarisse Sieckenius de Souza, Carla Faria Leitão, Raquel Oliveira Prates, Sílvia Amélia Bim, and Elton José da Silva. 2010. Can inspection methods generate valid new knowledge in HCI? The case of semiotic inspection. International Journal of Human-Computer Studies 68, 1-2 (2010), 22–40.
Clarisse Sieckenius De Souza, Carla Faria Leitão, Raquel Oliveira Prates, and Elton José Da Silva. 2006. The semiotic inspection method. In Proceedings of VII Brazilian symposium on Human factors in computing systems. 148–157.
Anind K Dey. 2001. Understanding and using context. Personal and ubiquitous computing 5 (2001), 4–7.
Jean Garcia-Gathright, Brian St. Thomas, Christine Hosey, Zahra Nazari, and Fernando Diaz. 2018. Understanding and evaluating user satisfaction with music discovery. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 55–64.
Ron Garland. 1991. The mid-point on a rating scale: Is it desirable. Marketing bulletin 2, 1 (1991), 66–70.
G Geetha, M Safa, C Fancy, and D Saranya. 2018. A hybrid approach using collaborative filtering and content based filtering for recommender system. In Journal of Physics: Conference Series. IOP Publishing.
Michael Gillhofer and Markus Schedl. 2015. Iron maiden while jogging, debussy for dinner? An analysis of music listening behavior in context. In MultiMedia Modeling: 21st International Conference, MMM 2015, Sydney, NSW, Australia, January 5-7, 2015, Proceedings, Part II 21. Springer, 380–391.
Yike Guo, Chao Wu, and Diego Peteiro-Barral. 2012. An EEG-Based brain informatics application for enhancing music experience. In International Conference on Brain Informatics. Springer, 265–276.
Marc Hassenzahl. 2010. Experience design: Technology for all the right reasons. Synthesis lectures on human-centered informatics 3, 1 (2010), 1–95.
Karim M Ibrahim, Jimena Royo-Letelier, Elena V Epure, Geoffroy Peeters, and Gael Richard. 2020. Audio-based auto-tagging with contextual tags for music. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 16–20.
Elliot Jenkins and Yanyan Yang. 2016. Creating a music recommendation and streaming application for android. In International Conference on Database and Expert Systems Applications. Springer, 201–215.
Chengkun Jiang and Yuan He. 2016. Smart-dj: Context-aware personalization for music recommendation on smartphones. In 2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 133–140.
Yucheng Jin, Nyi Nyi Htun, Nava Tintarev, and Katrien Verbert. 2019. ContextPlay: Evaluating User Control for Context-Aware Music Recommendation. In Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization. 294–302.
Yucheng Jin, Nava Tintarev, Nyi Nyi Htun, and Katrien Verbert. 2020. Effects of personal characteristics in control-oriented user interfaces for music recommender systems. User Modeling and User-Adapted Interaction 30 (2020), 199–249.
Robert Johns. 2005. One size doesn’t fit all: Selecting response scales for attitude items. Journal of Elections, Public Opinion & Parties 15, 2 (2005), 237–264.
Dehui Luo, Xiang Wan, Jiming Liu, and Tiejun Tong. 2018. Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range. Statistical methods in medical research 27, 6 (2018), 1785–1805.
Maake Benard Magara, Sunday Ojo, Seleman Ngwira, and Tranos Zuva. 2016. MPlist: Context aware music playlist. In 2016 IEEE International Conference on Emerging Technologies and Innovative Business Practices for the Transformation of Societies (EmergiTech). IEEE, 309–316.
Erica R Oliveira, Luiz CS Luz, and Raquel O Prates. 2008. Aplicação semi-estruturada do método de inspeção semiótica: estudo de caso para o domínio educacional. In Proceedings of the VIII Brazilian Symposium on Human Factors in Computing Systems. 50–59.
Rohaila Mohd Rosly and Fariza Khalid. 2018. Evaluation of the “e-Daftar” system using the Technology Acceptance Model (TAM). Creative Education 9, 5 (2018), 675–686.
Elena Shakirova. 2017. Collaborative filtering for music recommender system. In 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). IEEE, 548–550.
Tiancheng Shen, Jia Jia, Yan Li, Yihui Ma, Yaohua Bu, Hanjie Wang, Bo Chen, Tat-Seng Chua, and Wendy Hall. 2020. Peia: Personality and emotion integrated attentive model for music recommendation on social media platforms. In Proceedings of the AAAI conference on artificial intelligence, Vol. 34. 206–213.
Keith S Taber. 2018. The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in science education 48 (2018), 1273–1296.
Gabriel Viana Teixeira and Luciana AM Zaina. 2022. Using Lean Personas to the Description of UX-related Requirements: A Study with Software Startup Professionals.. In ICEIS (2). 211–222.
Viswanath Venkatesh and Hillol Bala. 2008. Technology acceptance model 3 and a research agenda on interventions. Decision sciences 39, 2 (2008), 273–315.
Viswanath Venkatesh and Fred D Davis. 2000. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science 46, 2 (2000), 186–204.
Viswanath Venkatesh, Michael G Morris, Gordon B Davis, and Fred D Davis. 2003. User acceptance of information technology: Toward a unified view. MIS quarterly (2003), 425–478.
Xinxi Wang, David Rosenblum, and Ye Wang. 2012. Context-aware mobile music recommendation for daily activities. In Proceedings of the 20th ACM international conference on Multimedia. 99–108.
Eva Zangerle and Christine Bauer. 2022. Evaluating Recommender Systems: Survey and Framework. Comput. Surveys 55, 8 (2022), 1–38.
Qian Zhao. 2018. User-Centric Design and Evaluation of Online Interactive Recommender Systems. (2018).
Publicado
16/10/2023
Como Citar
ASSUNÇÃO, Willian Garcias; PRATES, Raquel Oliveira; ZAINA, Luciana Aparecida Martinez.
From User Context to Tailored Playlists: A User Centered Approach to Improve Music Recommendation System. In: SIMPÓSIO BRASILEIRO SOBRE FATORES HUMANOS EM SISTEMAS COMPUTACIONAIS (IHC), 22. , 2023, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
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