##### 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.

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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This is a medium difficulty Statistics & Math 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.

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

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.

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.

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

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

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

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?