Maksims Volkovs
Maksims Volkovs
layer6.ai
Verified email at layer6.ai - Homepage
TitleCited byYear
Boltzrank: learning to maximize expected ranking gain
MN Volkovs, RS Zemel
Proceedings of the 26th Annual International Conference on Machine Learning …, 2009
1002009
Continuous data cleaning
M Volkovs, F Chiang, J Szlichta, RJ Miller
Data Engineering (ICDE), 2014 IEEE 30th International Conference on, 244-255, 2014
682014
A flexible generative model for preference aggregation
MN Volkovs, RS Zemel
Proceedings of the 21st international conference on World Wide Web, 479-488, 2012
612012
Collaborative ranking with 17 parameters
M Volkovs, RS Zemel
Advances in Neural Information Processing Systems, 2294-2302, 2012
502012
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
392011
New learning methods for supervised and unsupervised preference aggregation
MN Volkovs, RS Zemel
The Journal of Machine Learning Research 15 (1), 1135-1176, 2014
362014
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
262015
Learning to rank by aggregating expert preferences
MN Volkovs, H Larochelle, RS Zemel
Proceedings of the 21st ACM international conference on Information and …, 2012
182012
Efficient sampling for bipartite matching problems
M Volkovs, RS Zemel
Advances in Neural Information Processing Systems, 1313-1321, 2012
152012
Content-based Neighbor Models for Cold Start in Recommender Systems
M Volkovs, GW Yu, T Poutanen
Proceedings of the Recommender Systems Challenge 2017, 7, 2017
112017
Context models for web search personalization
M Volkovs
arXiv preprint arXiv:1502.00527, 2015
112015
DropoutNet: Addressing Cold Start in Recommender Systems
M Volkovs, G Yu, T Poutanen
Advances in Neural Information Processing Systems, 4957-4966, 2017
102017
CRF framework for supervised preference aggregation
MN Volkovs, RS Zemel
Proceedings of the 22nd ACM international conference on Information …, 2013
102013
Loss-sensitive training of probabilistic conditional random fields
MN Volkovs, H Larochelle, RS Zemel
arXiv preprint arXiv:1107.1805, 2011
92011
Two-stage approach to item recommendation from user sessions
M Volkovs
Proceedings of the 2015 International ACM Recommender Systems Challenge, 3, 2015
82015
ConEx: a system for monitoring queries
C Mishra, M Volkovs
Proceedings of the 2007 ACM SIGMOD international conference on Management of …, 2007
82007
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, 9, 2018
22018
Multi-tiered information retrieval training
CJC Burges, KM Svore, M Volkovs
US Patent App. 12/974,704, 2012
12012
Noise Contrastive Estimation for Scalable Linear Models for One-Class Collaborative Filtering
G Wu, M Volkovs, CL Soon, S Sanner, H Rai
arXiv preprint arXiv:1811.00697, 2018
2018
Learning document embeddings with convolutional neural network architectures
M Volkovs, TJ Poutanen
US Patent App. 15/863,612, 2018
2018
The system can't perform the operation now. Try again later.
Articles 1–20