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--- seminars:datasci:191010 [2019/09/26 20:38] – created qiao
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+<WRAP centeralign>##Data Science Seminar##\\ Hosted by Department of Mathematical Sciences</WRAP>
+  * Date: Thursday, October 24, 2019
+  * Time: 1:15 -- 2:15 pm
+  * Room: WH-100E
+  * Speaker: Douglas Turnbull (Ithaca College)
+  * Title: Locally-focused Music Recommendation
+<WRAP center box 80%>
+<WRAP centeralign>**//Abstract//**</WRAP>{{ :seminars:datasci:turnbull.jpg?direct 180x|}}
+There are talented musicians all around us. They play amazing live shows at small venues in every city all around the world. Yet music services like Spotify, Apple Music, YouTube, and Pandora do a poor job of helping listeners discover these artists for a variety of commercial and technical reasons.
+In this talk, I will discuss my recent efforts to use recommender systems to support locally-focused music discovery. First, I will provide a brief introduction to recommender systems, the long-tail consumption models, and popularity bias. I'll then describe how we can adapt typical recommender system algorithms to be better at recommending local (long-tail) music. Finally, I will describe a personalized Internet radio project called MegsRadio.fm, why it failed after years of dedicated development, and how lessons learned are being incorporated into the design of my new project called Localify.org.
+</WRAP>
+Bio:
+Doug Turnbull is an associate professor in the Department of Computer Science at Ithaca College in upstate New York, USA. His main research interests include music information retrieval, computer audition, machine learning, and recommender systems. He is currently a board member of the International Society of Music Information Retrieval (ISMIR.) More information about his research can be found at https://dougturnbull.org/