[ONLINE] Douglas Eck: "Music recommendation and discovery at scale"
The rebroadcast of a Distinguished Lecture by a guest from Google (USA) followed by an online live discussion with Prof. Eck himself.
About the event
This session will feature the rebroadcast of the lecture presented by Douglas Eck on September 30, 2014 followed by a one-hour live discussion with Prof. Eck himself. The main goal is to revisit the topic, and then, in the discussion that will follow, evaluate what has changed since the research was first presented. Participants are encouraged to submit their questions and comments in the chat of the platform used.
To access the event: www.cirmmt.org/join/DL1 (platform and details coming soon).
I will discuss recent work done at Google to tackle the problem of music recommendation in a streaming music service. I'll look at some strategies for using audio features as a means to improve quality and to go deep into the long tail. I'll also look at embeddings-based methods for collaborative filtering. Finally I'll discuss the use of Knowledge Graph as a means for providing structured data about the world of music. An overarching theme in the talk is, "What do listeners actually want from a music streaming service?" I don't have a complete answer for this, but think it's worth talking about, especially since it motivates collaborations across domains relevant for CIRMMT. Though I'll address some of the technical and algorithmic details involved in building a music recommendation system, the talk is geared for a general audience.
Staff Research Scientist, Google, Mountain View
Douglas leads a team carrying out research in music recommendations, search and discovery. Before coming to Google, Douglas was an associate professor in Computer Science at the University of Montreal where he worked with the LISA machine learning lab, the Centre for Research in Brain Music and Sound (BRAMS) and the Centre for Interdisciplinary Research in Music Media and Technology (CIRMMT).