| Autoencoders D Bank, N Koenigstein, R Giryes Machine learning for data science handbook: data mining and knowledge …, 2023 | 1073 | 2023 |
| Item2vec: neural item embedding for collaborative filtering O Barkan, N Koenigstein 2016 IEEE 26th International Workshop on Machine Learning for Signal …, 2016 | 674 | 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 | 389 | 2011 |
| The Yahoo! Music Dataset and KDD-Cup’11 G Dror, N Koenigstein, Y Koren, M Weimer KDD-Cup Workshop 2011, 2011 | 361 | 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 | 195 | 2014 |
| One-class collaborative filtering with random graphs U Paquet, N Koenigstein Proceedings of the 22nd international conference on World Wide Web, 999-1008, 2013 | 114 | 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 | 108 | 2012 |
| Low-rank factorization of determinantal point processes M Gartrell, U Paquet, N Koenigstein Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 95 | 2017 |
| 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 | 86 | 2019 |
| Bayesian low-rank determinantal point processes M Gartrell, U Paquet, N Koenigstein Proceedings of the 10th ACM Conference on Recommender Systems, 349-356, 2016 | 69 | 2016 |
| Forecasting CPI inflation components with hierarchical recurrent neural networks O Barkan, J Benchimol, I Caspi, E Cohen, A Hammer, N Koenigstein International Journal of Forecasting 39 (3), 1145-1162, 2023 | 58 | 2023 |
| Autoencoders. arXiv D Bank, N Koenigstein, R Giryes arXiv preprint arXiv:2003.05991, 2593-2613, 2020 | 58 | 2020 |
| Towards scalable and accurate item-oriented recommendations N Koenigstein, Y Koren Proceedings of the 7th ACM conference on Recommender systems, 419-422, 2013 | 55 | 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 | 54 | 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 | 53 | 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 | 52 | 2013 |
| 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 | 51 | 2016 |
| Web-scale media recommendation systems G Dror, N Koenigstein, Y Koren Proceedings of the IEEE 100 (9), 2722-2736, 2012 | 44 | 2012 |
| 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 | 40 | 2020 |
| Partial VAE for hybrid recommender system C Ma, W Gong, JM Hernández-Lobato, N Koenigstein, S Nowozin, ... NIPS Workshop on Bayesian Deep Learning 2018, 2018 | 37 | 2018 |
Oren BarkanThe Open UniversityVerified email at openu.ac.il
