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.

2. 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.