Séminaire Donnees et APprentissage Artificiel
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Machine Learning @dailymotion : Toward better content understanding and more accurate recommendation
Intervenant(s) : Yves Mabiala (Dailymotion)In this talk I will describe two of the main subjects the data science team at Dailymotion is focusing on. I will first start by describing how a video is automatically characterized in terms of verticals (sport, music, ...) and topics (coming from wikipedia) using multi-modal approaches based on the sound and the images of the video but also the text characterizing it. In a second step, I will describe how we are able to pick out of a 250 million video catalog the most accurate videos for millions of users especially using sequence models for session-based recommendation
Yves Mabiala is a data scientist leading the data science team at Dailymotion. He is currently working working on large scale recommendation problems and content characterization from raw signals (audio, video). Prior to Dailymotion he was working at Thales as a research scientist in the data science lab where he was focusing on large scale unsupervised anomaly detection in cyber-security, credit card fraud detection or unsupervised sequence learning especially applied to predictive maintenance. He was also a member of the LIP6/Thales joint lab, where he was working with the ComplexNetwork team on studying the dynamics of large graphs but also with MLIA team on time series representation learning.
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