Interpretable and Scalable Recommender SystemsSpeaker: Michalis Vlachos – Lausanne, Switzerland
Topic(s): Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing
AbstractIn 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 LectureNumber of Slides: 50
Duration: 50 minutes
Languages Available: English
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