Measurement and applications of position bias in a marketplace search engine
This work addresses position bias for consumer marketplace search engines, but it is incremental as it builds on existing methods for bias measurement and adaptation.
The paper tackled the problem of position bias in a marketplace search engine by conducting a randomization program to measure its impact, which led to improved models and enabled user-facing scenario tooling.
Search engines intentionally influence user behavior by picking and ranking the list of results. Users engage with the highest results both because of their prominent placement and because they are typically the most relevant documents. Search engine ranking algorithms need to identify relevance while incorporating the influence of the search engine itself. This paper describes our efforts at Thumbtack to understand the impact of ranking, including the empirical results of a randomization program. In the context of a consumer marketplace we discuss practical details of model choice, experiment design, bias calculation, and machine learning model adaptation. We include a novel discussion of how ranking bias may not only affect labels, but also model features. The randomization program led to improved models, motivated internal scenario analysis, and enabled user-facing scenario tooling.