Analyze View Distribution and Recommendation Overlap in Videos

Quick Overview

This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Analyze View Distribution and Recommendation Overlap in Videos states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Analyze View Distribution and Recommendation Overlap in Videos

Company: Meta

Role: Data Scientist

Category: Statistics & Math

Difficulty: medium

Interview Round: Onsite

##### Scenario Analyzing video-level view distribution and recommendation overlap in a short-video platform. ##### Question Given view counts per video, draw or describe the distribution; report mode, median, mean, and 99th percentile. Two users frequently share identical videos in their top-10 list. Statistically evaluate whether this is desirable or signals a problem. ##### Hints Discuss heavy-tailed (Zipf-like) shape, long tail, diversity vs. homogeneity trade-offs.

Quick Answer: This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Analyze View Distribution and Recommendation Overlap in Videos states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Aug 4, 2025, 10:55 AM
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Analyze View Distribution and Recommendation Overlap in Videos

Short-Video Platform: View Distribution and Recommendation Overlap

Context

You are analyzing a short-video platform. You have:

  • A dataset of per-video view counts over a fixed time window (e.g., last 30 days).
  • Two users whose top-10 recommended videos (or top-10 consumed videos) frequently include identical items.

Assume view counts are nonnegative integers and video identity is deduplicated (e.g., by content hash, not just URL) to avoid counting re-uploads separately.

Tasks

  1. Distribution of video-level views
  • Describe how you would visualize the distribution of views per video.
  • Report the mode, median, mean, and 99th percentile of the distribution.
  1. Overlap in top-10 videos between two users
  • Statistically evaluate whether frequent overlap in two users' top-10 lists is desirable or a potential problem.
  • Explicitly consider heavy-tail effects (Zipf-like distributions), and discuss trade-offs between diversity and homogeneity.

Hints

  • Expect a heavy-tailed, long-tail distribution (often Zipf/Pareto-like).
  • Weigh personalization and diversity against the benefits of showing trending, high-quality content.

Clarifying Questions to Ask Guidance

  • Clarify the random variables, distributional assumptions, independence assumptions, and desired output.
  • Show enough derivation for the interviewer to follow the reasoning.
  • Explain how you would validate the result with simulation or sensitivity checks.

What a Strong Answer Covers Guidance

  • A correct setup with definitions, formulas, and boundary conditions.
  • A step-by-step derivation or estimation plan.
  • Interpretation of the result, including uncertainty and practical limitations.
  • Checks for assumptions, edge cases, and numerical stability.

Follow-up Questions Guidance

  • How would the result change if the assumptions were relaxed?
  • Can you verify the answer with a simulation?
  • What is the most likely source of estimation error?
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