Òscar Celma is currently VP of Data Science at Pandora, where he leads a team of scientists and musicologists to provide the best personalized audio experience tuned to the moment you’re in. From 2011 till 2014 Òscar was Senior Research Scientist at Gracenote. His work focused on music and video recommendation and discovery. Before that he was co-founder and Chief Innovation Officer at Barcelona Music and Audio Technologies (BMAT). Òscar published a book named “Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space” (Springer, 2010). In 2008, Òscar obtained his Ph.D. in Computer Science and Digital Communication, in the Pompeu Fabra University (Barcelona, Spain). He holds a few patents from his work on music discovery as well as on Vocaloid, a singing voice-synthesizer bought by Yamaha in 2004.
Upcoming Abstract Summary
‘The Voice’: New Challenges in a Zero UI world
The adoption of voice-enabled devices has seen an explosive growth in the last few years and music consumption is among the most popular use cases. Music personalization and recommendation plays a major role at Pandora in providing a daily delightful listening experience for millions of users. In turn, providing the same perfectly tailored listening experience through these novel voice interfaces brings new interesting challenges and exciting opportunities. In this talk we will describe how we apply personalization and recommendation techniques in three common voice scenarios which can be defined in terms of request types: known-item, thematic, and broad open-ended. We will describe how we use deep learning slot filling techniques and query classification to interpret the user intent and identify the main concepts in the query.
We will also present the differences and challenges regarding evaluation of voice powered recommendation systems. Since pure voice interfaces do not contain visual UI elements, relevance labels need to be inferred through implicit actions such as play time, query reformulations or other types of session level information. Another difference is that while the typical recommendation task corresponds to recommending a ranked list of items, a voice play request translates into a single item play action. Thus, some considerations about closed feedback loops need to be made. In summary, improving the quality of voice interactions in music services is a relatively new challenge and many exciting opportunities for breakthroughs still remain. There are many new aspects of recommendation system interfaces to address to bring a delightful and effortless experience for voice users. We will share a few open challenges to solve for the future.