Adjust YouTube Ad Scores Using Mixed-Effects Linear Regression
Quick Overview
Evaluates mixed-effects regression for debiasing ad ratings from lenient or severe reviewers. Strong answers specify ad fixed effects, reviewer random effects, adjusted scores, validation, and ordinal alternatives.
Adjust YouTube Ad Scores Using Mixed-Effects Linear Regression
Company: Google
Role: Data Scientist
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
##### Scenario
100 reviewers each rate 100 YouTube ads on a 1–10 scale; scores must be adjusted for reviewer bias.
##### Question
Propose a modeling framework that produces unbiased ad scores and justify why a mixed-effects linear regression is appropriate.
##### Hints
Treat reviewer as random effect and ad as fixed effect.
Quick Answer: Evaluates mixed-effects regression for debiasing ad ratings from lenient or severe reviewers. Strong answers specify ad fixed effects, reviewer random effects, adjusted scores, validation, and ordinal alternatives.
Adjusting YouTube Ad Scores with Mixed-effects Regression
One hundred reviewers each rate the same 100 YouTube ads on a 1 to 10 scale. Some reviewers may systematically rate higher or lower than others because of leniency or severity bias.
Produce unbiased ad scores on a common scale by removing reviewer bias. Propose a modeling framework and justify why mixed-effects linear regression is appropriate.
Constraints & Assumptions
Treat the 1 to 10 rating as approximately continuous for the main model.
Use ad-specific scores and reviewer-specific bias terms.
Reviewers are considered a sample from a larger reviewer population.
Mention alternatives if the ordinal nature of ratings is important.
Clarifying Questions to Ask Guidance
Did every reviewer rate every ad, or are there missing ratings?
Are reviewers sampled randomly or selected for expertise?
Are ads from categories or campaigns that should be modeled hierarchically?
Is the goal ranking ads, estimating absolute scores, or debiasing individual ratings?
Part 1 - Modeling Framework
Specify the mixed-effects model.
What This Part Should Cover Guidance
Define rating y_ij for reviewer i and ad j.
Include ad fixed effects for ad scores.
Include reviewer random intercepts for leniency or severity bias.
Include residual error and identifiability constraints.
Part 2 - Why Mixed Effects
Justify why mixed-effects linear regression is appropriate.
What This Part Should Cover Guidance
Explain repeated ratings by reviewer and ad.
Explain partial pooling for reviewer bias estimates.
Treat reviewers as a sample from a broader population while estimating ad scores on a common scale.
Handle unbalanced ratings if some reviewer-ad combinations are missing.
Part 3 - Debiased Scores and Validation
Explain how to produce and validate unbiased ad scores.
What This Part Should Cover Guidance
Use estimated ad fixed effects, adjusted for reviewer random effects, to score ads.