Interpretable and Scalable Recommender Systems

Speaker:  Michalis Vlachos – Lausanne, Switzerland
Topic(s):  Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing


In this talk I describe a new class of recommendation techniques based on co-clustering, which is not only fast and accurate but it is also interpretable. I also show how GPU-accelerated versions of these algorithms can lead to interactive training of recommender systems. 

About this Lecture

Number of Slides:  50
Duration:  50 minutes
Languages Available:  English
Last Updated: 

Request this Lecture

To request this particular lecture, please complete this online form.

Request a Tour

To request a tour with this speaker, please complete this online form.

All requests will be sent to ACM headquarters for review.