Neural Learning To Rank by Bhaskar Mitra (Guest lecture at Emory University)

Learning to rank (LTR) for information retrieval (IR) involves the application of machine learning models to rank artifacts, such as items to be recommended, in response to user’s need. LTR models typically employ training data, such as human relevance labels and click data, to discriminatively train towards an IR objective. The focus of this tutorial will be on the fundamentals of neural networks and their applications to learning to rank.

Slides: https://www.slideshare.net/BhaskarMitra3/neural-learning-to-rank-231759858
Next lecture: https://www.youtube.com/watch?v=y-6OJzLZgEE

Speaker Bios

Bhaskar Mitra is a Principal Applied Scientist at Bing in Montreal, Canada. He joined Microsoft in 2006 and Bing—then called Live Search—in 2007. Before moving to Montreal, he has been part of the Microsoft labs in Hyderabad (India), Bellevue (USA), and Cambridge (UK). His research interests include machine learning and information retrieval, and in particular the topic of neural information retrieval. He co-organized multiple workshops and tutorials, served as a guest editor for the special issue of the Information Retrieval Journal, and co-authored a book on the topic of neural information retrieval. He is currently a doctoral graduand at University College London under the supervision of Dr. Emine Yilmaz and Dr. David Barber.