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 | 1052 | 2010 |
Data mining methods for recommender systems X Amatriain, JM Pujol Recommender systems handbook, 39-71, 2011 | 437 | 2011 |
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 | 407 | 2010 |
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 | 361 | 2008 |
Towards time-dependant recommendation based on implicit feedback L Baltrunas, X Amatriain Workshop on context-aware recommender systems (CARS’09), 25-30, 2009 | 335 | 2009 |
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 | 299 | 2009 |
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 | 223 | 2009 |
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 | 221 | 2009 |
Netflix recommendations: Beyond the 5 stars (part 1) X Amatriain, J Basilico Netflix Tech Blog 6, 2012 | 218 | 2012 |
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 | 177* | 2000 |
Mining large streams of user data for personalized recommendations X Amatriain ACM SIGKDD Explorations Newsletter 14 (2), 37-48, 2013 | 171 | 2013 |
Big & personal: data and models behind netflix recommendations X Amatriain Proceedings of the 2nd international workshop on big data, streams and …, 2013 | 150 | 2013 |
Recommender systems in industry: A netflix case study X Amatriain, J Basilico Recommender systems handbook, 385-419, 2015 | 140 | 2015 |
Spectral processing X Amatriain, J Bonada, A Loscos, X Serra DAFX: Digital Audio Effects, 373-438, 2002 | 124* | 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 | 115 | 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 | 89 | 2016 |
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 | 87 | 2010 |
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 | 76* | 2019 |
The science behind the Netflix algorithms that decide what you’ll watch next T Vanderbilt Wired Magazine 21 (8), 08, 2013 | 76 | 2013 |
The allosphere: a large-scale immersive surround-view instrument T Höllerer, JA Kuchera-Morin, X Amatriain Proceedings of the 2007 workshop on Emerging displays technologies: images …, 2007 | 75 | 2007 |