| Autoencoders D Bank, N Koenigstein, R Giryes Machine learning for data science handbook: data mining and knowledge …, 2023 | 1297 | 2023 |
| Item2vec: neural item embedding for collaborative filtering O Barkan, N Koenigstein 2016 IEEE 26th international workshop on machine learning for signal …, 2016 | 713 | 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 | 395 | 2011 |
| The Yahoo! Music Dataset and KDD-Cup’11 G Dror, N Koenigstein, Y Koren, M Weimer KDD-Cup Workshop 2011, 2011 | 371 | 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 | 196 | 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 | 116 | 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 | 106 | 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 | 100 | 2019 |
| Low-rank factorization of determinantal point processes M Gartrell, U Paquet, N Koenigstein Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017 | 99 | 2017 |
| 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 | 83 | 2023 |
| Bayesian low-rank determinantal point processes M Gartrell, U Paquet, N Koenigstein proceedings of the 10th ACM conference on recommender systems, 349-356, 2016 | 71 | 2016 |
| Autoencoders. arxiv D Bank, N Koenigstein, R Giryes arXiv preprint arXiv:2003.05991, 2593-2613, 2020 | 70 | 2020 |
| Machine learning for data science handbook: data mining and knowledge discovery handbook D Bank, N Koenigstein, R Giryes Autoencoders, 353-374, 2023 | 57* | 2023 |
| 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 |
| 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 | 54 | 2016 |
| 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 | 53 | 2015 |
| 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 | 53 | 2013 |
| 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 |
| Grad-sam: Explaining transformers via gradient self-attention maps O Barkan, E Hauon, A Caciularu, O Katz, I Malkiel, O Armstrong, ... Proceedings of the 30th ACM International Conference on Information …, 2021 | 48 | 2021 |
| 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 | 46 | 2020 |
Oren BarkanThe Open UniversityZweryfikowany adres z openu.ac.il