#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

• Bram van den Akker on LinkedIn
• Practical Bandits: An Industry Perspective (Website)
• Practical Bandits: An Industry Perspective (Recording)
• Tutorial at The Web Conference 2020: Unbiased Learning to Rank: Counterfactual and Online Approaches
• Tutorial at RecSys 2021: Counterfactual Learning and Evaluation for Recommender Systems: Foundations, Implementations, and Recent Advances
• GitHub: Open Bandit Pipeline

Papers:

• van den Akker et al. (2023): Practical Bandits: An Industry Perspective
• van den Akker et al. (2022): Extending Open Bandit Pipeline to Simulate Industry Challenges
• van den Akker et al. (2019): ViTOR: Learning to Rank Webpages Based on Visual Features

General Links:

• Follow me on LinkedIn
• Follow me on X
• Send me your comments, questions and suggestions to [email protected]
• Recsperts Website