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How would you design a Shop Ads ranking algorithm?

MetaData Scientist · Technical Screen
EasyData ScientistTechnical ScreenMachine Learning

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This problem tests the ability to design an end-to-end ad ranking system, focusing on objective optimization, feature engineering, and mitigating common biases in marketplace environments.

Scenario

You are tasked with designing a ranking system for Shop Ads on a large e-commerce platform. When a user searches for a product, the platform needs to select and order sponsored items to display in the search results. The goal is to maximize total platform utility, which balances user experience, advertiser ROI, and long-term ecosystem health.

The Task

Provide a high-level system design for this ranking algorithm. Your response should address the following areas:

  1. Objective Function Formulation: How do you define the 'utility' of an ad slot? Consider the competing interests of the user (relevance), the advertiser (conversions/value), and the platform (revenue).
  2. Modeling Approach: Describe a machine learning architecture to predict the relevant metrics (e.g., Click-Through Rate, Conversion Rate) for a given user-item-query triplet.
  3. Candidate Generation & Ranking: Briefly explain how you would narrow down millions of potential ads to the few shown on the results page.
  4. Bias and Fairness: Identify one significant source of bias in the training data (e.g., position bias or selection bias) and describe how you would mitigate it.
  5. Evaluation: How would you measure the success of a new model update before and after a full-scale deployment?

Hints

Hint 1

Consider the difference between predicting user behavior (clicks/conversions) and optimizing for platform-wide revenue.

Hint 2

Think about how position bias occurs: users are more likely to click the top result regardless of the ad's objective quality.

Hint 3

Discuss how to handle the cold-start problem for new ads that have no historical performance data.

Follow-up questions

  • How would you handle the trade-off between exploration (showing new ads) and exploitation (showing high-performing ads)?
  • If advertisers are over-bidding for irrelevant keywords, how would you adjust the ranking formula to protect user experience?
  • How does the latency of conversion data (e.g., a user clicks now but buys three days later) impact your model training pipeline?