How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

What difficulty level is this interview question?

This is a hard difficulty Machine Learning question, commonly asked during Technical Screen rounds at Meta.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Meta during technical interviews.

How to design Shop ad ranking | Meta Interview Question

Quick Overview

This question evaluates a candidate's expertise in machine learning and data science for ad ranking systems, including objective formulation and trade-offs among user value, advertiser value, and platform revenue, choice of training labels (clicks, add-to-cart, purchases, long-term value), feature engineering, cold-start and advertiser-size handling, fairness, and robustness to feedback loops and gaming. It is commonly asked to assess ability to design and evaluate production recommender/advertising models; domain: Machine Learning / Ad Ranking / Recommender Systems; level of abstraction: practical application with system-level conceptual reasoning and measurement-focused evaluation (offline and online).

Suppose the experiment suggests that increasing exposure for Shop ads may be beneficial. The interviewer then asks how you would design the ranking algorithm.

Design a ranking approach for Shop ads on a Meta surface. The system must decide when a Shop ad should rank above a regular website ad.

Discuss:

the optimization objective and tradeoffs among user value, advertiser value, and platform revenue;
the labels or training targets you would use, including click, add-to-cart, purchase, long-term value, and possible offline outcomes;
important features for the user, ad, Shop quality, advertiser quality, and context;
how you would handle small versus large advertisers, cold start, and fairness;
how you would evaluate the model offline and online;
how you would manage feedback loops, exploration, calibration, and advertiser gaming.

How to design Shop ad ranking

Quick Overview

Solution

Comments (0)