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Xavier Amatriain
Xavier Amatriain
VP of Product, Core ML/AI. Google
Adresse e-mail validée de amatriain.net - Page d'accueil
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Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering
A Karatzoglou, X Amatriain, L Baltrunas, N Oliver
Proceedings of the fourth ACM conference on Recommender systems, 79-86, 2010
10612010
Data mining methods for recommender systems
X Amatriain, JM Pujol
Recommender systems handbook, 39-71, 2011
4402011
Temporal diversity in recommender systems
N Lathia, S Hailes, L Capra, X Amatriain
Proceedings of the 33rd international ACM SIGIR conference on Research and …, 2010
4102010
Watching television over an IP network
M Cha, P Rodriguez, J Crowcroft, S Moon, X Amatriain
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement, 71-84, 2008
3612008
Towards time-dependant recommendation based on implicit feedback
L Baltrunas, X Amatriain
Workshop on context-aware recommender systems (CARS’09), 25-30, 2009
3352009
I like it... i like it not: Evaluating user ratings noise in recommender systems
X Amatriain, JM Pujol, N Oliver
International Conference on User Modeling, Adaptation, and Personalization …, 2009
3032009
The wisdom of the few: a collaborative filtering approach based on expert opinions from the web
X Amatriain, N Lathia, JM Pujol, H Kwak, N Oliver
Proceedings of the 32nd international ACM SIGIR conference on Research and …, 2009
2262009
Rate it again: increasing recommendation accuracy by user re-rating
X Amatriain, JM Pujol, N Tintarev, N Oliver
Proceedings of the third ACM conference on Recommender systems, 173-180, 2009
2242009
Netflix recommendations: Beyond the 5 stars (part 1)
X Amatriain, J Basilico
Netflix Tech Blog 6, 2012
2202012
Towards instrument segmentation for music content description a critical review of instrument classification techniques
H Boyer, X Amatriain, E Batlle, X Serra
Proceedings of the 1st International Symposium on Music Information …, 2000
178*2000
Mining large streams of user data for personalized recommendations
X Amatriain
ACM SIGKDD Explorations Newsletter 14 (2), 37-48, 2013
1722013
Big & personal: data and models behind netflix recommendations
X Amatriain
Proceedings of the 2nd international workshop on big data, streams and …, 2013
1562013
Recommender systems in industry: A netflix case study
X Amatriain, J Basilico
Recommender systems handbook, 385-419, 2015
1432015
Spectral processing
X Amatriain, J Bonada, A Loscos, X Serra
DAFX: Digital Audio Effects, 373-438, 2002
125*2002
Medically aware GPT-3 as a data generator for medical dialogue summarization
B Chintagunta, N Katariya, X Amatriain, A Kannan
Machine Learning for Healthcare Conference, 354-372, 2021
1232021
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
902016
Weighted content based methods for recommending connections in online social networks
R Garcia-Gavilanes, X Amatriain
Workshop on Recommender Systems and the Social Web in the 2010 ACM Recsys …, 2010
872010
Few-shot learning for dermatological disease diagnosis
V Prabhu, A Kannan, M Ravuri, M Chaplain, D Sontag, X Amatriain
Machine Learning for Healthcare Conference, 532-552, 2019
80*2019
Dr. summarize: Global summarization of medical dialogue by exploiting local structures
A Joshi, N Katariya, X Amatriain, A Kannan
arXiv preprint arXiv:2009.08666, 2020
762020
The science behind the Netflix algorithms that decide what you’ll watch next
T Vanderbilt
Wired Magazine 21 (8), 08, 2013
762013
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