Netflix Interview Question: How to Design a Metric to Compare Rankings of Lists of Shows

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Netflix Interview Question: How to Design a Metric to Compare Rankings of Lists of Shows

When preparing for data science or data analyst interviews, one common type of question you’ll encounter is around metrics design. These questions test both your statistical reasoning and your ability to apply math to real-world business problems.

One such question, famously asked by Netflix, is:

“How would you design a metric to compare rankings of lists of shows for a given user?”

This question is deceptively simple. At first glance, it looks like a vague math puzzle. But in reality, it’s a product analytics question with a heavy emphasis on statistical thinking.

In this post, we’ll take a deep dive into how to approach this question step by step:

Understanding the context of the problem.
Defining why we need such a metric.
Exploring potential approaches.
Comparing different mathematical metrics.
Building intuition with examples.
Concluding with trade-offs and real-world applications.

Whether you’re a student preparing for interviews, or a professional brushing up on analytics case studies, this breakdown will help you build confidence in tackling similar open-ended problems.

Step 1: Understanding the Problem Context

Let’s carefully parse the question:

Netflix serves personalized rankings of shows and movies to each user. For example, when you log in, you see a carousel like:

Stranger Things
Breaking Bad
The Crown
Squid Game
Money Heist

These rankings are based on recommendation algorithms that consider your past viewing history, watch time, ratings, and similar users’ behavior.

Now, suppose Netflix runs two recommendation algorithms (say, an old version and a new experimental model). For each user, both algorithms will output a ranked list of shows.

The question is asking:

👉 How do we design a metric that can compare how “different” (or “similar”) these two lists are for the same user?

This is critical because Netflix can’t just launch new algorithms blindly — they need to measure impact, stability, and consistency across rankings.

Step 2: Why Do We Need This Metric?

Before proposing metrics, always ask: What business problem does this solve?

For Netflix, comparing rankings helps in:

Evaluating new algorithms: If a new recommendation engine ranks “Squid Game” at #10 instead of #2, does it matter?
Measuring user experience impact: Small ranking shifts might not matter, but big changes for popular shows might frustrate users.
Detecting stability issues: If two algorithms produce wildly different rankings, that could signal a bug or bias.
A/B testing: During experiments, Netflix wants to see if ranking differences actually lead to higher engagement.

So the goal of the metric is to quantify:

Similarity (are the rankings aligned?)
Significance (are the changes meaningful?)
Interpretability (can we explain it to business teams?)

Step 3: Potential Approaches

It's a good idea to familiarize yourself with ranking in statistics and the different statistical tests related to it. You can read this here.

There are multiple ways to compare rankings mathematically. Let’s walk through the major families of methods:

1. Exact Match Comparison

Simply count how many shows appear in the exact same position.
Example:
- List A: [Stranger Things, Breaking Bad, The Crown]
- List B: [Stranger Things, The Crown, Breaking Bad]
- Only the first position matches.

This approach is too rigid. A small swap makes it look very different even if the lists are almost identical.

2. Overlap of Sets

Compare which shows appear in the top N positions.
For instance, look at the top 10 shows in each list and compute the Jaccard similarity:

\(Jaccard(A, B) = \frac{|A \cap B|}{|A \cup B|}\)

This ignores the exact order, but captures whether users are exposed to the same set of shows.

Pros: Simple, interpretable.
Cons: Doesn’t account for ranking differences within the set.

3. Rank Correlation Metrics

This is where things get interesting. Two main measures are widely used:

(a) Spearman’s Rank Correlation

Measures how well the relationship between two rankings can be described using a monotonic function.
Formula:

\(\rho = 1 - \frac{6 \sum d_i^2}{n(n^2 - 1)}\)

where did_idi = difference between ranks of item i.

If lists are identical → ρ = 1.
If completely reversed → ρ = -1.

Good for: Capturing global correlation between rankings.

(b) Kendall’s Tau

Looks at pairwise agreements/disagreements between rankings.
Formula:

\(\tau = \frac{(C - D)}{\binom{n}{2}}\)

where C = concordant pairs, D = discordant pairs.

If most pairs agree in relative ordering → high τ.

Good for: Capturing local differences in ranking order.

4. Position-Weighted Metrics

Netflix might care more about differences at the top of the list (since that’s what users see first).

Examples:

NDCG (Normalized Discounted Cumulative Gain): Common in information retrieval. It penalizes ranking mistakes at the top more heavily.
Formula:

\(DCG = \sum_{i=1}^n \frac{rel_i}{\log_2(i+1)}\)

Here relirel_ireli could represent show importance (like watch probability).

Then normalize by the ideal DCG.

Pros: Reflects real-world importance of top recommendations.
Cons: Requires relevance scores.

Step 4: Building Intuition with an Example

Let’s say we have:

Old algorithm ranking (A): [Stranger Things, Breaking Bad, The Crown, Squid Game, Money Heist]
New algorithm ranking (B): [Stranger Things, The Crown, Money Heist, Breaking Bad, Squid Game]

Overlap of Top 5:

Same shows appear, so Jaccard = 1.0.

Spearman’s Correlation:

Ranks:
- Breaking Bad (A=2, B=4 → diff=2)
- The Crown (A=3, B=2 → diff=1)
- Squid Game (A=4, B=5 → diff=1)
- Money Heist (A=5, B=3 → diff=2)

\(\sum d_i^2 = 2^2 + 1^2 + 1^2 + 2^2 = 10 \\ \rho = 1 - \frac{6(10)}{5(25-1)} = 1 - \frac{60}{120} = 0.5\)

So correlation = 0.5 → moderately aligned.

Kendall’s Tau:

Check pairwise swaps:
- Breaking Bad vs The Crown → disagreement.
- Breaking Bad vs Money Heist → disagreement.
- Squid Game vs Money Heist → disagreement.

Out of 10 pairs, maybe 7 agree, 3 disagree → τ ≈ 0.4.

NDCG:

If we assign relevance = 1 to all shows:

\(DCG(A) = 1 + 1/log2(3) + 1/log2(4) + 1/log2(5) + 1/log2(6)\)
Compare with DCG(B).
Result might be very similar since sets are the same, just slightly reordered.

Step 5: What’s the Best Metric?

Now the key: What should you propose in an interview?

The best approach depends on Netflix’s goals:

If they only care about overlap (same shows recommended): Use Jaccard similarity.
If they care about global alignment: Use Spearman’s rank correlation.
If they care about top positions more: Use NDCG or a weighted Kendall’s Tau.
If they want interpretability for product managers: Simpler metrics like overlap of top 10 shows are easier to explain.

In practice, Netflix likely uses a combination of metrics:

NDCG to focus on user-facing impact.
Spearman/Kendall for stability checks.
Overlap for sanity checks.

Step 6: Real-World Applications

This type of metric design has broader applications beyond Netflix:

Spotify: Comparing music playlists for personalization.
Amazon: Comparing product recommendation lists.
YouTube: Evaluating ranking changes in video recommendations.
LinkedIn: Measuring changes in ranked job recommendations.

In all these cases, companies want to ensure that ranking changes improve engagement without introducing instability.

Step 7: Structuring Your Interview Answer

Here’s how you can structure your answer in an interview:

Clarify the problem: Are we comparing algorithms? Or measuring user impact?
Define goals: Similarity, stability, interpretability.
Discuss options: Jaccard, Spearman, Kendall, NDCG.
Propose a solution: Recommend NDCG + Spearman’s correlation as a balanced approach.
Mention trade-offs: Simpler metrics are easier to explain, complex metrics capture nuance.
Conclude with business value: These metrics help Netflix improve personalization while keeping user trust.

Conclusion

The Netflix question, “How would you design a metric to compare rankings of lists of shows for a given user?” is not just about math. It’s a holistic product analytics problem.

The key is to:

Break down the problem.
Explore multiple metric families.
Propose a balanced, interpretable solution.
Tie it back to business goals and user experience.

By practicing questions like this, you’ll be ready for real-world data interviews where open-ended, ambiguous problems are the norm.

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