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This is a medium 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.

Evaluate Product-Ranking Algorithm with Precision and Recall Metrics | Meta Interview Question

Evaluate Product-Ranking Algorithm with Precision and Recall Metrics

Last updated: Mar 29, 2026

Quick Overview

This question evaluates competency in assessing product‑ranking models with emphasis on ranking metrics, precision and recall interpretations, experiment metric design, and the ability to map model outputs to user‑level funnels and business KPIs.

Scenario

Instagram Shopping wants to improve its product‑ranking algorithm for the shopping feed. The goal is to select and order products for each user to maximize value (e.g., conversions/revenue) while maintaining a good user experience.

Task

Propose offline metrics to evaluate a ranking model for a shopping feed and explain why they fit this use case.
Propose online (experiment) metrics, including how you would structure user‑level funnels.
Explain how precision and recall translate to ranking in this setting (including @K variants).
Explain how you would reconcile offline metrics with on‑site business KPIs.

Hints (for scope)

Consider NDCG and MAP for offline ranking evaluation.
Consider click‑through, conversion lift, and funnels for online evaluation.

Solution

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