Shedding Light on Positional Bias: Strategies for Mitigation

People often click on the first things they see, not just because they’re relevant, but because of their position and because Google wants us to scroll less and click on the first ad items. While working on ML-based ranking, we often algorithmically promote things that are already quite popular, making them even more popular and building a true self-reinforcing bad search. In this talk, we will discuss how to deal with these biases and how to make them improve the search quality and not ruin it. How to make the ranking model itself less biased? Is it possible to remove biases from the training data? While working on a search platform for a global food delivery service, we performed many experiments in this area and hit all the pitfalls - and can share real-world test results for comparison

Burak Isikli

Delivery Hero

Burak Isikli is working as a Staff Machine Learning Engineer for Delivery Hero. Currently, his work focuses on ML approaches to search ranking and personalization on how to improve search relevance