An open source platform to assist the creation of group playlists through artificial intelligence algorithms

  • Flaviano Dias Fontes Universidade Federal de Pernambuco
  • Giordano Ribeiro Eulalio Cabral Universidade Federal de Pernambuco
  • Geber Lisboa Ramalho Universidade Federal de Pernambuco

Resumo


Recommendation systems are a constantly expanding study area, with applications in various fields such as e-commerce, films, music to promote the user’s suggestions. When we talk about music, we have more than 20 years of studies trying to solve the problem of a good generation of playlists that maximizes the satisfaction of a larger number of listeners. For automated automatic playlist generation methods focusing on a user group, we have the collaborative filter as a more assertive method to get the user’s not likely, to improve the performance of group recommendation algorithms we store the preferences of users Especially I did not like it by placing the availability of using this data as an algorithm input parameter. The platform described in This paper is intended to facilitate testing between these recommendation systems, standardizing data entry, and facilitating requests. The use of GraphQL as a framework associated with Apollo as a library, greatly facilitates the integration of these APIs, as the separation of data sources makes it possible to associate Spotify data with Deezer or Apple Music data, these data are stored in the database of the connection, so that in future requests it will no longer be necessary to consult the Spotify API, thus facilitating the consumption of data from the artificial intelligence algorithms, as well as a possible sharing of songs between services, since all services have an ISRC code to identify the songs.

Palavras-chave: Artificial Intelligence, A-Life and Evolutionary Music Systems, Music, Society, and Technology, Software Systems and Languages for Sound and Music

Referências

Arnt Maasø and Anja Nylund Hagen. Metrics and decisionmaking in music streaming. Popular Communication, 18(1):18–31, 2020.

Martijn Millecamp, Nyi Nyi Htun, Yucheng Jin, and Katrien Verbert. Controlling spotify recommendations: effects of personal characteristics on music recommender user interfaces. In Proceedings of the 26th Conference on user modeling, adaptation and personalization, pages 101– 109, 2018.

Spotify web api. https://developer.spotify.com/documentation/web-api/. Accessed: 202012-22.

Francesco Ricci, Lior Rokach, and Bracha Shapira. Introduction to recommender systems handbook. In Recommender systems handbook, pages 1–35. Springer, 2011.

Sriharsha Dara, C Ravindranath Chowdary, and Chintoo Kumar. A survey on group recommender systems. Journal of Intelligent Information Systems, 54(2):271–295, 2020.

Conor Hayes, Pádraig Cunningham, Patrick Clerkin, and Marco Grimaldi. Programme-driven music radio. In Proc. of the ECAI, volume 2, 1996.

Joseph F McCarthy and Theodore D Anagnost. Musicfx: an arbiter of group preferences for computer supported collaborative workouts. In Proceedings of the 1998 ACM conference on Computer supported cooperative work, pages 363–372, 1998.

Apple Music documentation. https://developer.apple.com/documentation/applemusicapi/. Accessed: 2021-05-12.

Deezer api documentation. https://developers.deezer.com. Accessed: 2021-02-15.

MusicKit documentation. https://developer.apple.com/documentation/musickitjs/. Accessed: 2021-05-12.

Andrew Crossen, Jay Budzik, and Kristian J Hammond. Flytrap: intelligent group music recommendation. In Proceedings of the 7th international conference on Intelligent user interfaces, pages 184–185, 2002.

Ingrid A Christensen and Silvia Schiaffino. Entertainment recommender systems for group of users. Expert systems with applications, 38(11):14127–14135, 2011.

Mike Gartrell, Xinyu Xing, Qin Lv, Aaron Beach, Richard Han, Shivakant Mishra, and Karim Seada. Enhancing group recommendation by incorporating social relationship interactions. In Proceedings of the 16th ACM international conference on Supporting group work, pages 97– 106, 2010.

Sarik Ghazarian and Mohammad Ali Nematbakhsh. Enhancing memory-based collaborative filtering for group recommender systems. Expert systems with applications, 42(7):3801–3812, 2015.

Apollo framework. https://www.apollographql.com. Accessed: 2021-06-20.

Wei Zhou, Li Li, Min Luo, and Wu Chou. Rest api design patterns for sdn northbound api. In 2014 28th international conference on advanced information networking and applications workshops, pages 358–365. IEEE, 2014.

Creating an apple music api token for musickit js. [link]. Accessed: 2021-06-20.

Audio features object. [link]. Accessed: 2021-02-15.

Ruben Taelman, Miel Vander Sande, and Ruben Verborgh. Graphql-ld: linked data querying with graphql. In ISWC2018, the 17th International Semantic Web Conference, pages 1–4, 2018.
Publicado
24/10/2021
FONTES, Flaviano Dias; CABRAL, Giordano Ribeiro Eulalio; RAMALHO, Geber Lisboa. An open source platform to assist the creation of group playlists through artificial intelligence algorithms. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO MUSICAL (SBCM), 18. , 2021, Recife. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 166-169. DOI: https://doi.org/10.5753/sbcm.2021.19442.