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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 Onsite rounds at Meta.

What role is this question designed for?

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).

Quick Overview

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

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

Submit Your Answer to Earn 20XP

Optimize IG Shopping ranking with multiple objectives

Quick Overview

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

Solution

Submit Your Answer to Earn 20XP