Machine Learning Group
Department of Computer Science
University of Toronto
About Me
I graduated with a Ph.D. in Machine Learning, previously I was a member of the Machine Learning Group working under the supervision of Richard Zemel.
Research Interests
My research interests broadly span the field of machine learning with emphasis on information retrieval and collaborative filtering.
In particular, I'm interested in machine learning methods for search (learning-to-rank, personalization etc.),
preference aggregation and recommender systems.
I participated in several data mining competitions on Kaggle:
-3'rd prize in Yandex Personalized Web Search Challenge.
Large scale web search personalization challenge based on the Yandex search log dataset containing over 160M records from
5.7M users and 21M queries. 194 teams participated in this challenge,
generating over 3.5 thousand submissions. You can read about my approach here.
-2'nd place in Million Song Dataset Challenge. Large scale collaborative
ranking challenge based on the Million Song Dataset with listening
histories for 1.1M users and 380K songs. 150 teams participated in this challenge, generating over 900 submissions.
Publications
- Context Models For Web Search Personalization
Maksims N. Volkovs
WSDM-2014: WSCD Workshop on Log-based Personalization
[pdf] - New Learning Methods for Supervised and Unsupervised Preference Aggregation
Maksims N. Volkovs and Richard S. Zemel
JMLR-2014: Journal of Machine Learning Research
[pdf] - Continuous Data Cleaning
Maksims N. Volkovs, Fei Chiang, Jaroslaw Szlichta and Renée J. Miller
ICDE-2014: IEEE International Conference on Data Engineering
[pdf] - Supervised CRF Framework for Preference Aggregation
Maksims N. Volkovs and Richard S. Zemel
CIKM-2013: International Conference on Information and Knowledge Management
[pdf][code] - Collaborative Ranking with 17 Parameters
Maksims N. Volkovs and Richard S. Zemel
NIPS-2012: Neural Information Processing Systems
[pdf] - Efficient Sampling for Bipartite Matching Problems
Maksims N. Volkovs and Richard S. Zemel
NIPS-2012: Neural Information Processing Systems
[pdf][supplementary] - Learning to Rank By Aggregating Expert Preferences
Maksims N. Volkovs, Hugo Larochelle and Richard S. Zemel
CIKM-2012: International Conference on Information and Knowledge Management
[pdf][code] - A Flexible Generative Model for Preference Aggregation
Maksims N. Volkovs and Richard S. Zemel
WWW-2012: International World Wide Web Conference
[pdf][code] - Learning to Rank with Multiple Objective Functions
Krysta M. Svore, Maksims N. Volkovs and Christopher J. C. Burges
WWW-2011: International World Wide Web Conference
[pdf] - BoltzRank: Learning to Maximize Expected Ranking Gain (Best student paper)
Maksims N. Volkovs and Richard S. Zemel
ICML-2009: International Conference on Machine Learning
[pdf] - ConEx: A System for Monitoring Queries
Chaitanya Mishra and Maksims N. Volkovs
SIGMOD-2007: International Conference on Management of Data
[pdf]
Patents
- Multi-Tiered Information Retrieval Training
Christopher J. C. Burges, Krysta M. Svore and Maksims N. Volkovs
US Patent App. 12/974,704.
