| Item2vec: neural item embedding for collaborative filtering O Barkan, N Koenigstein 2016 IEEE 26th International Workshop on Machine Learning for Signal …, 2016 | 441 | 2016 |
| Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy N Koenigstein, G Dror, Y Koren Proceedings of the fifth ACM conference on Recommender systems, 165-172, 2011 | 360 | 2011 |
| The Yahoo! Music Dataset and KDD-Cup’11 G Dror, N Koenigstein, Y Koren, M Weimer KDD-Cup Workshop 2011, 2011 | 309 | 2011 |
| Speeding up the xbox recommender system using a euclidean transformation for inner-product spaces Y Bachrach, Y Finkelstein, R Gilad-Bachrach, L Katzir, N Koenigstein, ... Proceedings of the 8th ACM Conference on Recommender systems, 257-264, 2014 | 140 | 2014 |
| Autoencoders D Bank, N Koenigstein, R Giryes arXiv preprint arXiv:2003.05991, 2020 | 133 | 2020 |
| One-class collaborative filtering with random graphs U Paquet, N Koenigstein Proceedings of the 22nd international conference on World Wide Web, 999-1008, 2013 | 106 | 2013 |
| Efficient retrieval of recommendations in a matrix factorization framework N Koenigstein, P Ram, Y Shavitt Proceedings of the 21st ACM international conference on Information and …, 2012 | 85 | 2012 |
| Low-rank factorization of determinantal point processes M Gartrell, U Paquet, N Koenigstein Thirty-First AAAI Conference on Artificial Intelligence, 2017 | 75 | 2017 |
| Bayesian low-rank determinantal point processes M Gartrell, U Paquet, N Koenigstein Proceedings of the 10th ACM Conference on Recommender Systems, 349-356, 2016 | 54 | 2016 |
| Towards scalable and accurate item-oriented recommendations N Koenigstein, Y Koren Proceedings of the 7th ACM conference on Recommender systems, 419-422, 2013 | 51 | 2013 |
| Efficient modeling system for user recommendation using matrix factorization N Nice, N Koenigstein, U Paquet, S Keren, D Sitton, D Kremer, S Roitman US Patent 8,983,888, 2015 | 47 | 2015 |
| Xbox movies recommendations: Variational Bayes matrix factorization with embedded feature selection N Koenigstein, U Paquet Proceedings of the 7th ACM Conference on Recommender Systems, 129-136, 2013 | 47 | 2013 |
| Selecting content-based features for collaborative filtering recommenders R Ronen, N Koenigstein, E Ziklik, N Nice Proceedings of the 7th ACM conference on Recommender systems, 407-410, 2013 | 43 | 2013 |
| Web-scale media recommendation systems G Dror, N Koenigstein, Y Koren Proceedings of the IEEE 100 (9), 2722-2736, 2012 | 43 | 2012 |
| CB2CF: a neural multiview content-to-collaborative filtering model for completely cold item recommendations O Barkan, N Koenigstein, E Yogev, O Katz Proceedings of the 13th ACM Conference on Recommender Systems, 228-236, 2019 | 40* | 2019 |
| Beyond collaborative filtering: The list recommendation problem O Sar Shalom, N Koenigstein, U Paquet, HP Vanchinathan Proceedings of the 25th international conference on world wide web, 63-72, 2016 | 35 | 2016 |
| Song Ranking based on Piracy in Peer-to-Peer Networks. N Koenigstein, Y Shavitt ISMIR, 633-638, 2009 | 32 | 2009 |
| Feature embedding in matrix factorization N Nice, N Koenigstein, U Paquet, SZ Keren, A Jaffray US Patent App. 13/723,539, 2014 | 29 | 2014 |
| A large-scale exploration of group viewing patterns AJB Chaney, M Gartrell, JM Hofman, J Guiver, N Koenigstein, P Kohli, ... Proceedings of the ACM International Conference on Interactive Experiences …, 2014 | 27 | 2014 |
| Scalable attentive sentence pair modeling via distilled sentence embedding O Barkan, N Razin, I Malkiel, O Katz, A Caciularu, N Koenigstein Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3235-3242, 2020 | 26 | 2020 |
Oren BarkanVerified email at microsoft.com
