Monthly Archives: November 2018

Class meeting #12 – Music recommendation algorithms II: Collaborative filtering – Monday 11/26

Readings:

Erik Bernhardsson. 2104. “Music Recommendations @ MLConf 2014.” Technology, April 14. https://www.slideshare.net/erikbern/music-recommendations-mlconf-2014.
Bell, R., Y. Koren, and C. Volinsky. 2009. “Matrix Factorization Techniques for Recommender Systems.” Computer, 2009. https://datajobs.com/data-science-repo/Recommender-Systems-%5bNetflix%5d.pdf.
Levy, Mark, and Klaas Bosteels. 2010. “Music Recommendation and the Long Tail.” In Proceedings of the Workshop on Music Recommendation and Discovery (WOMRAD), 55–58. http://ceur-ws.org/Vol-633/wom2010_paper10.pdf.

Response:

The principal assumption behind collaborative filtering for music recommendation is that your listening choices act as an implicit signal not just about your preferences, also the preferences of “listeners like you”. In a short repsonse based on your reading of the two papers (and skimming the slides prepared by an ex-Spotify employee), what are some of the challenges faced by the designers of recommendation systems that use collaborative filtering, and, if possible, suggest ways that this technique can be improved or used in tandem with other approaches to recommendation in order to overcome these problems?

Class meeting #11 – Artificial musical agents – Monday 11/19

Readings:

Weizenbaum, Joseph. 1966. “ELIZA—a Computer Program for the Study of Natural Language Communication Between Man and Machine.” Commun. ACM 9 (1): 36–45. https://doi.org/10.1145/365153.365168.
Lewis, George E. 1999. “Interacting with Latter-Day Musical Automata.” Contemporary Music Review 18 (3): 99–112.
“Magenta Wins ‘Best Demo’ at NIPS 2016!” n.d. Magenta. Accessed August 6, 2018. https://magenta.tensorflow.org/2016/12/16/nips-demo.
“Learning from A.I. Duet.” n.d. Accessed August 6, 2018. https://magenta.tensorflow.org/2017/02/16/ai-duet.

Reponse:

In a short response, contrast Weizenbaum’s design for an artificial interlocutor with Lewis’s. How can ideological differences be reflected in the construction of the software systems that they propose? What are the fundamental differences between designing a system for intelligent verbal conversation and for musical improvisation?

Class meeting #10 – Music recommendation algorithms I: Content-based – Monday 11/12

Readings:

Sturm, Bob L. 2014. “The State of the Art Ten Years After a State of the Art: Future Research in Music Information Retrieval.” Journal of New Music Research 43 (2): 147–72. https://doi.org/10.1080/09298215.2014.894533.
Drott, Eric A. 2018. “Music as a Technology of Surveillance.” Journal of the Society for American Music 12 (3): 233–67. https://doi.org/10.1017/S1752196318000196.
Askin, Noah, and Michael Mauskapf. 2017. “What Makes Popular Culture Popular? Product Features and Optimal Differentiation in Music.” American Sociological Review 82 (5): 910–44. https://doi.org/10.1177/0003122417728662.

Response:

No written response required, but be prepared to discuss the Drott as the main target piece for the seminar.