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

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

Optimize IG Shopping ranking with multiple objectives

Quick Overview

This question evaluates multi-objective ranking and recommender-system competencies, including predictive modeling and calibration, constrained objective aggregation, seller-level fairness constraints, fraud robustness, selection-bias-aware training, and latency-aware on-device deployment.

Instagram Shopping: Multi-Objective Ranking With Fairness, Fraud Robustness, and On-Device Constraints

You are designing the Instagram Shopping home feed ranking to jointly optimize buyer GMV, buyer satisfaction, and seller fairness. Propose a production-ready multi-objective ranking framework that addresses:

Modeling: Predict (a) purchase probability, (b) return risk, and (c) post-purchase satisfaction.
Fairness: Enforce seller-level minimum exposure constraints and support cold-start seller exploration.
Integrity: Be robust to fraudulent sellers and fake engagement.
Latency: Support on-device prefetching with a 20 ms budget.

Describe:

Model family and calibration (e.g., multi-task heads, calibrated scores).
Objective aggregation (e.g., constrained optimization vs. learned utility) and how it is computed online.
Training data sampling to counter selection bias from historical rankings.
Post-launch monitoring for fairness and fraud drift, and how to throttle supply during spikes (e.g., flash sales).

Optimize IG Shopping ranking with multiple objectives

Quick Overview

Instagram Shopping: Multi-Objective Ranking With Fairness, Fraud Robustness, and On-Device Constraints

Solution

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