Maksims Volkovs

PhD, Machine Learning Group
Department of Computer Science
University of Toronto
maksims [dot] volkovs [at] gmail [dot] com

Photo


ABOUT ME

I completed my PhD in 2013 at the University of Toronto where I was a member of the Machine Learning Group working with Richard Zemel. My research interests broadly span the field of machine learning with emphasis on information retrieval, recommender systems and NLP. I'm a co-founder of layer6.ai, a start-up that focuses on machine learning for the enterprise.

I actively participate in data science competitions on Kaggle and other platforms, here are some highlights:

PUBLICATIONS

  • Content-based Neighbor Models for Cold Start in Recommender Systems
    Maksims Volkovs, Guang Wei Yu and Tomi Poutanen
    RecSys-2017: ACM Conference on Recommender Systems
    [pdf]
  • Two-Stage Approach to Item Recommendation from User Sessions
    Maksims Volkovs
    RecSys-2015: ACM Conference on Recommender Systems
    [pdf]
  • Effective Latent Models for Binary Feedback in Recommender Systems
    Maksims Volkovs and Guang Wei Yu
    SIGIR-2015: Special Interest Group on Information Retrieval
    [pdf] [code]
  • Context Models For Web Search Personalization
    Maksims Volkovs
    WSDM-2014: WSCD Workshop on Log-based Personalization
    [arXiv: 1502.00527]
  • New Learning Methods for Supervised and Unsupervised Preference Aggregation
    Maksims 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 Volkovs and Richard S. Zemel
    NIPS-2012: Neural Information Processing Systems
    [pdf]
  • Efficient Sampling for Bipartite Matching Problems
    Maksims Volkovs and Richard S. Zemel
    NIPS-2012: Neural Information Processing Systems
    [pdf][supplementary]
  • Learning to Rank By Aggregating Expert Preferences
    Maksims 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 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 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 Volkovs and Richard S. Zemel
    ICML-2009: International Conference on Machine Learning
    [pdf]
  • ConEx: A System for Monitoring Queries
    Chaitanya Mishra and Maksims Volkovs
    SIGMOD-2007: International Conference on Management of Data
    [pdf]

PATENTS

  • Deep Collaborative Filtering
    Maksims Volkovs and Tomi Poutanen
    US Patent App.
  • System and Method for Providing a Search Engine, and a Graphical User Interface Therefor
    Maksims Volkovs, William Hemming, Francesco Petruzzelli and Andrew Curran
    US Patent App.
  • Method and System Utilizing Collaborative Filtering
    Maksims Volkovs and Tomi Poutanen
    US Patent App.
  • Multi-Tiered Information Retrieval Training
    Christopher J. C. Burges, Krysta M. Svore and Maksims Volkovs
    US Patent App.