Combining Collaborative and Content Filtering in a Recommendation System for a Web-based DAW

            
@inproceedings{2019_30,
  abstract = {EarSketch is a web-based audio production and education platform that uses an online coding environment and the Web Audio API to teach introductory programming and music production to students. One of the main challenges of implementing an educational online music production platform is providing users with a variety of foundational audio loops to use in order to foster creative personal expression. EarSketch aims to achieve this through the inclusion of a sound browser for users to navigate and select sounds to use as part of their compositions. This paper describes the implementation and evaluation of a hybrid recommendation engine, combining collaborative and content filtering, designed to guide users through the sound browser and promote diversity in student compositions. The paper also presents a preliminary analysis of the impact of different recommendation strategies on user sound selection, and how the application of recommendation strategies can inform the design of EarSketch and other web-based DAWs.},
  address = {Trondheim, Norway},
  author = {Smith, Jason and Jacob, Mikhail and Freeman, Jason and Magerko, Brian and Mcklin, Tom},
  booktitle = {Proceedings of the International Web Audio Conference},
  editor = {Xambó, Anna and Martín, Sara R. and Roma, Gerard},
  month = {December},
  pages = {53--58},
  publisher = {NTNU},
  series = {WAC '19},
  title = {Combining Collaborative and Content Filtering in a Recommendation System for a Web-based DAW},
  year = {2019},
  ISSN = {2663-5844}
}