Dr. Georgia Koutrika is Director of Research at Athena Research Center in Greece. In the past, she worked at HP Labs, at IBM Research-Almaden, and as a postdoctoral researcher at the Computer Science Dept., Stanford University. She has received a PhD and a diploma in Computer Science from the University of Athens in Greece. Her work is in the broader area of big data and in the intersection of databases, information retrieval, and machine learning, and involves: data exploration, recommendation systems, user analytics, and large-scale information extraction, entity resolution and information integration. Her work has been incorporated in commercial products, has been described in 8 granted patents and 18 patent applications in the US and worldwide, and has been published in more than 80 research papers in top-tier conferences and journals. An IEEE Senior member and ACM Senior member, Dr. Koutrika is an ACM SIGMOD Associate Information Director and editor of ACM SIGMOD Blog, and ACM Distinguished Speaker. She has served as Demo PC co-chair for ACM SIGMOD 2018, General Co-Chair for ACM SIGMOD 2016, Industrial Track PC Chair for EDBT 2016, and Workshop and Tutorial Co-Chair for IEEE ICDE 2016. She is an Associate Editor for VLDB 2019 and 2020. She serves in the program committees of top-tier conferences, and she has organized several focused workshops in databases, personalization and data exploration.
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Deep learning in recommender systems
Deep learning methods have dramatically improved the state-of-the-art in computer vision, speech recognition, natural language processing (NLP) and many other domains. Deep learning started to...
Modern recommender systems in action (I know what movie you will watch in Netflix)
Recommender systems provide advice on items that may be of interest to a user (e.g., movies, products, travel, and leisure activities) by learning user preferences and relationships between users...
Modern recommender systems: matrices, bandits, and neurons
The proliferation of digital content in a plurality of forms (including e-news, movies, and online courses), along with the popularity of portable devices has created immense opportunities as well...
Multi-armed bandits in recommender systems
Traditional recommender systems can provide meaningful recommendations at an individual level by leveraging users' interests as demonstrated by their past activity. However, in many web-based...
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