| 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 | 1007 | 2010 |
| Data mining methods for recommender systems X Amatriain, JM Pujol Recommender systems handbook, 39-71, 2011 | 409 | 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 | 391 | 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 | 358 | 2008 |
| Towards time-dependant recommendation based on implicit feedback L Baltrunas, X Amatriain Workshop on context-aware recommender systems (CARS’09), 25-30, 2009 | 321 | 2009 |
| I like it... i like it not: Evaluating user ratings noise in recommender systems X Amatriain, JM Pujol, N Oliver User Modeling, Adaptation, and Personalization: 17th International …, 2009 | 282 | 2009 |
| Netflix recommendations: Beyond the 5 stars (part 1) X Amatriain, J Basilico Netflix Tech Blog 6, 2012 | 217 | 2012 |
| 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 | 212 | 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 | 201 | 2009 |
| 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 | 183 | 2000 |
| Mining large streams of user data for personalized recommendations X Amatriain ACM SIGKDD Explorations Newsletter 14 (2), 37-48, 2013 | 163 | 2013 |
| Big & personal: data and models behind netflix recommendations X Amatriain Proceedings of the 2nd international workshop on big data, streams and …, 2013 | 133 | 2013 |
| Spectral processing X Amatriain, J Bonada, A Loscos, X Serra DAFX: Digital Audio Effects, 373-438, 2002 | 121* | 2002 |
| Recommender systems in industry: A netflix case study X Amatriain, J Basilico Recommender systems handbook, 385-419, 2015 | 103 | 2015 |
| Weighted content based methods for recommending connections in online social networks R Garcia-Gavilanes, X Amatriain Association for Computing Machinery, 2010 | 82 | 2010 |
| 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 | 72 | 2016 |
| The science behind the Netflix algorithms that decide what you’ll watch next T Vanderbilt Wired, 2013 | 72 | 2013 |
| Content-based transformations X Amatriain, J Bonada, lex Loscos, JL Arcos, V Verfaille Journal of New Music Research 32 (1), 95-114, 2003 | 72 | 2003 |
| Implicit feedback recommendation via implicit-to-explicit ordinal logistic regression mapping D Parra, A Karatzoglou, X Amatriain, I Yavuz Proceedings of the CARS-2011 5, 2011 | 70 | 2011 |
| The allosphere: Immersive multimedia for scientific discovery and artistic exploration X Amatriain, JA Kuchera-Morin, T Hollerer, ST Pope IEEE MultiMedia 16 (02), 64-75, 2009 | 70 | 2009 |
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