Justin Basilico
Justin Basilico
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Unifying collaborative and content-based filtering
J Basilico, T Hofmann
Proceedings of the twenty-first international conference on Machine learning, 9, 2004
Netflix recommendations: Beyond the 5 stars (part 1)
X Amatriain, J Basilico
Netflix Tech Blog 6, 2012
Recommender systems in industry: A netflix case study
X Amatriain, J Basilico
Recommender systems handbook, 385-419, 2015
Deep learning for recommender systems: A Netflix case study
H Steck, L Baltrunas, E Elahi, D Liang, Y Raimond, J Basilico
AI Magazine 42 (3), 7-18, 2021
Past, present, and future of recommender systems: An industry perspective
X Amatriain, J Basilico
Proceedings of the 10th ACM conference on recommender systems, 211-214, 2016
Artwork personalization at Netflix
A Chandrashekar, F Amat, J Basilico, T Jebara
Netflix technology blog 7, 2017
A joint framework for collaborative and content filtering
J Basilico, T Hofmann
Proceedings of the 27th annual international ACM SIGIR conference on …, 2004
Artwork personalization at Netflix
F Amat, A Chandrashekar, T Jebara, J Basilico
Proceedings of the 12th ACM conference on recommender systems, 487-488, 2018
Comet: A recipe for learning and using large ensembles on massive data
JD Basilico, MA Munson, TG Kolda, KR Dixon, WP Kegelmeyer
2011 IEEE 11th international conference on data mining, 41-50, 2011
Using navigation to improve recommendations in real-time
CY Wu, CV Alvino, AJ Smola, J Basilico
Proceedings of the 10th ACM Conference on Recommender Systems, 341-348, 2016
Learning a personalized homepage
C Alvino, J Basilico
The Netflix Tech Blog 9, 2015
Deepqa jeopardy! gamification: a machine-learning perspective
AK Baughman, W Chuang, KR Dixon, Z Benz, J Basilico
IEEE transactions on computational intelligence and AI in games 6 (1), 55-66, 2013
Accordion: a trainable simulator for long-term interactive systems
J McInerney, E Elahi, J Basilico, Y Raimond, T Jebara
Proceedings of the 15th ACM Conference on Recommender Systems, 102-113, 2021
System architectures for personalization and recommendation
X Amatriain, J Basilico
Netflix Technology Blog 27, 2013
Recommending for the world
Y Raimond, J Basilico
Netflix tech blog 17, 2016
Media content rankings for discovery of novel content
JD Basilico
US Patent 9,430,532, 2016
Déja vu: The importance of time and causality in recommender systems
J Basilico, Y Raimond
Proceedings of the eleventh ACM conference on recommender systems, 342-342, 2017
Improved team performance using EEG-and context-based cognitive-state classifications for a vehicle crew
KR Dixon, K Hagemann, J Basilico, C Forsythe, S Rothe, M Schrauf, ...
Foundations of Augmented Cognition. Neuroergonomics and Operational …, 2009
Performance assessment to enhance training effectiveness
SM Stevens-Adams, JD Basilico, RG Abbott, CJ Gieseler, C Forsythe
Proceedings of the Interservice/Industry Training, Simulation, and Education …, 2010
The Cognitive Foundry: A Flexible Platform for Intelligent Agent Modeling.
JD Basilico, ZO Benz, KR Dixon
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2008
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