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Reinforcement Learning for Search

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Dealing with bias in search results - Roman Grebennikov

About this Event

We'll talk about using reinforcement learning in ranking problems

Outline:

  • Types of bias that can influence Learn-to-Rank algorithms: position and presentation biases
  • How to un-bias your training data with tricks like multi-armed-bandit feedback
  • Ways to build the LTR model over original biased data with inverse propensity scoring
  • How these approaches affect business metrics in e-commerce
  • Challenges in building an online-LTR system

About the speaker:

Roman Grebennikov is a seasoned DS engineer and startup enthusiast writing ML applications for food.

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