Search and Recommendation from Millions of Songs and Videos

Friday, May 8, 2015 - 10:05 am

Millions of songs and videos are available to all of us through the Internet. To allow users to retrieve the desired content, algorithms for automatic analysis, indexing and recommendation of this content are a must.

I will discuss some aspects of automated music analysis for music search and recommendation: i) automated music tagging (e.g., identify ``funky jazz with male vocals'' based on music audio), and ii) (audio) content-based music recommendation, to provide a list of relevant or similar song recommendations given one or more seed songs (e.g., playlist generation for online radio). Our most recent research on context-aware recommendation takes this one step further, by leveraging various wearable sensors (e.g., in smartphones) to infer user context (activity, mood) and provide recommendations accordingly, without requiring an active user query (zero click).

Finally, I will show how this technology can be readily extended to analyze and recommend video content for a variety of applications, integrating audio and visual cues.

Search and Recommendation from Millions of Songs and Videos - DataEDGE 2015

Associate Professor of Electrical and Computer Engineering,
UC San Diego

Gert Lanckriet is an associate professor of electrical and computer engineering at UC San Diego, where he currently heads the Computer Audition Laboratory (CALab) and leads an interdepartmental group on Computational Statistics and Machine Learning (COSMAL). His research interests are in data science, on the interplay between machine learning, applied statistics, and large-scale optimization, with applications to music and video search and recommendation, multimedia, and personalized, mobile health.

He was awarded the SIAM Optimization Prize in 2008 and is the recipient of a Hellman Fellowship, an IBM Faculty Award, an NSF CAREER Award, and an Alfred P. Sloan Foundation Research Fellowship. In 2011, MIT Technology Review named him one of the 35 top young technology innovators in the world (TR35). In 2014, he received the Best Ten-Year Paper Award at the International Conference on Machine Learning.

He co-founded Keevio, Inc., a content-based video analytics company, and Benefunder, an innovative organization that works with wealth management firms to connect philanthropists with leading researchers across the nation to fund their research. He received an MS in electrical engineering from the Katholieke Universiteit Leuven, Belgium, and an MS and PhD degrees in electrical engineering and computer science from UC Berkeley.