Define what “trending” means for articles and how to measure it. Specify the raw event schema you require (e.g., article_id, user_id, timestamp, action_type, dwell_time, referrer, device, locale). Propose a scoring formula that combines signals (impressions, clicks, dwell time, reactions, reshares) with recency decay, de-duplication, and spam/bot controls. Explain time windows (e.g., 5m, 1h, 24h), baseline normalization (e.g., by author follower count or historical traffic), category/locale segmentation, and cold-start handling. Outline offline evaluation (precision/recall against editorial ground truth, calibration) and online A/B tests, success metrics (CTR, dwell time uplift, save/share rates), and guardrails (session depth, bounce rate, creator fairness).

This question evaluates a software engineer's competency in analytics and experimentation, covering ranking-system design, signal engineering, data schema requirements, scoring frameworks, and evaluation metrics for article trending.

How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a hard difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Figma.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Figma during technical interviews.

Define and measure article trending | Figma Interview Question

Context

You are building a "Trending" ranking for an articles surface (e.g., home feed, explore). Define what “trending” means, what data you need, and how you would score, evaluate, and safely launch it.

Tasks

Definition
- Define “trending” for articles and how it differs from “popular” or “top”.
Data Requirements
- Specify the raw event schema you require (e.g., article_id, user_id, timestamp, action_type, dwell_time, referrer, device, locale).
Scoring
- Propose a scoring formula that combines signals (impressions, clicks, dwell time, reactions, reshares) with:
  - Recency decay
  - De-duplication (e.g., per user/session)
  - Spam/bot controls
Time and Normalization
- Explain time windows (e.g., 5m, 1h, 24h) and how to baseline-normalize (e.g., by author follower count, historical traffic).
- Describe category/locale segmentation and cold-start handling for new items.
Evaluation and Launch
- Outline offline evaluation (e.g., precision/recall against editorial ground truth, calibration).
- Outline online A/B tests, success metrics (CTR, dwell time uplift, save/share rates), and guardrails (session depth, bounce rate, creator fairness).

Tasks

Definition

Define “trending” for articles and how it differs from “popular” or “top”.

Data Requirements

Specify the raw event schema you require (e.g., article_id, user_id, timestamp, action_type, dwell_time, referrer, device, locale).

Scoring

Propose a scoring formula that combines signals (impressions, clicks, dwell time, reactions, reshares) with:
- Recency decay
- De-duplication (e.g., per user/session)
- Spam/bot controls

Time and Normalization

Explain time windows (e.g., 5m, 1h, 24h) and how to baseline-normalize (e.g., by author follower count, historical traffic).
Describe category/locale segmentation and cold-start handling for new items.

Evaluation and Launch

Outline offline evaluation (e.g., precision/recall against editorial ground truth, calibration).
Outline online A/B tests, success metrics (CTR, dwell time uplift, save/share rates), and guardrails (session depth, bounce rate, creator fairness).

Define and measure article trending

Quick Overview

Context

Tasks

Solution

Comments (0)

Define and measure article trending

Quick Overview

Context

Tasks

Solution

Comments (0)

Define and measure article trending

Quick Overview