#20: Practical Bandits and Travel Recommendations with Bram van den Akker

In episode 20 of Recsperts, we welcome Bram van den Akker, Senior Machine Learning Scientist at Booking.com. Bram's work focuses on bandit algorithms and counterfactual learning. He was one of the creators of the Practical Bandits tutorial at the World Wide Web conference. We talk about the role of bandit feedback in decision making systems and in specific for recommendations in the travel industry.

In our interview, Bram elaborates on bandit feedback and how it is used in practice. We discuss off-policy- and on-policy-bandits, and we learn that counterfactual evaluation is right for selecting the best model candidates for downstream A/B-testing, but not a replacement. We hear more about the practical challenges of bandit feedback, for example the difference between model scores and propensities, the role of stochasticity or the nitty-gritty details of reward signals. Bram also shares with us the challenges of recommendations in the travel domain, where he points out the sparsity of signals or the feedback delay.

At the end of the episode, we can both agree on a good example for a clickbait-heavy news service in our phones.

Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
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  • (00:00) - Introduction
  • (02:58) - About Bram van den Akker
  • (09:16) - Motivation for Practical Bandits Tutorial
  • (16:53) - Specifics and Challenges of Travel Recommendations
  • (26:19) - Role of Bandit Feedback in Practice
  • (49:13) - Motivation for Bandit Feedback
  • (01:00:54) - Practical Start for Counterfactual Evaluation
  • (01:06:33) - Role of Business Rules
  • (01:11:26) - better cut this section coherently
  • (01:17:48) - Rewards and More
  • (01:32:45) - Closing Remarks

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#20: Practical Bandits and Travel Recommendations with Bram van den Akker
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