| Boltzrank: learning to maximize expected ranking gain MN Volkovs, RS Zemel Proceedings of the 26th Annual International Conference on Machine Learning …, 2009 | 104 | 2009 |
| Continuous data cleaning M Volkovs, F Chiang, J Szlichta, RJ Miller 2014 IEEE 30th International Conference on Data Engineering, 244-255, 2014 | 85 | 2014 |
| A flexible generative model for preference aggregation MN Volkovs, RS Zemel Proceedings of the 21st international conference on World Wide Web, 479-488, 2012 | 65 | 2012 |
| Collaborative ranking with 17 parameters M Volkovs, RS Zemel Advances in neural information processing systems, 2294-2302, 2012 | 56 | 2012 |
| Learning to rank with multiple objective functions KM Svore, MN Volkovs, CJC Burges Proceedings of the 20th international conference on World wide web, 367-376, 2011 | 48 | 2011 |
| Effective latent models for binary feedback in recommender systems M Volkovs, GW Yu Proceedings of the 38th international ACM SIGIR conference on research and …, 2015 | 45 | 2015 |
| New learning methods for supervised and unsupervised preference aggregation MN Volkovs, RS Zemel The Journal of Machine Learning Research 15 (1), 1135-1176, 2014 | 39 | 2014 |
| Dropoutnet: Addressing cold start in recommender systems M Volkovs, G Yu, T Poutanen Advances in Neural Information Processing Systems, 4957-4966, 2017 | 31 | 2017 |
| Content-based neighbor models for cold start in recommender systems M Volkovs, GW Yu, T Poutanen Proceedings of the Recommender Systems Challenge 2017, 1-6, 2017 | 26 | 2017 |
| Learning to rank by aggregating expert preferences MN Volkovs, H Larochelle, RS Zemel Proceedings of the 21st ACM international conference on Information and …, 2012 | 23 | 2012 |
| Efficient sampling for bipartite matching problems M Volkovs, RS Zemel Advances in Neural Information Processing Systems, 1313-1321, 2012 | 18 | 2012 |
| Two-stage approach to item recommendation from user sessions M Volkovs Proceedings of the 2015 International ACM Recommender Systems Challenge, 1-4, 2015 | 14 | 2015 |
| Context models for web search personalization M Volkovs arXiv preprint arXiv:1502.00527, 2015 | 14 | 2015 |
| CRF framework for supervised preference aggregation MN Volkovs, RS Zemel Proceedings of the 22nd ACM international conference on Information …, 2013 | 12 | 2013 |
| Two-stage model for automatic playlist continuation at scale M Volkovs, H Rai, Z Cheng, G Wu, Y Lu, S Sanner Proceedings of the ACM Recommender Systems Challenge 2018, 1-6, 2018 | 10 | 2018 |
| Methods and Systems for Communicating Social Expression F Falcone, M Volkovs US Patent App. 13/345,205, 2012 | 10 | 2012 |
| Loss-sensitive training of probabilistic conditional random fields MN Volkovs, H Larochelle, RS Zemel arXiv preprint arXiv:1107.1805, 2011 | 10 | 2011 |
| ConEx: a system for monitoring queries C Mishra, M Volkovs Proceedings of the 2007 ACM SIGMOD international conference on Management of …, 2007 | 8 | 2007 |
| Explore-exploit graph traversal for image retrieval C Chang, G Yu, C Liu, M Volkovs Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019 | 4 | 2019 |
| Supervised CRF framework for preference aggregation MN Volkovs, RS Zemel International Conference on Information and Knowledge Management (CIKM), 89-98, 2013 | 4 | 2013 |
