gle Maksims Volkovs

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Machine Learning Group
Department of Computer Science
University of Toronto

E-mail


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.