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Netflix·Software Engineer·Technical Phone Screen·Senior

Senior
May 2026

Summary

Netflix coding round, one meaty algorithmic problem about finding pairs of users with overlapping watch histories. The problem had a follow-up asking about efficiency at scale, which is where things got interesting.

Questions Asked (1)

Q1

You have a dictionary mapping user IDs to their ordered movie-watching histories. Given integers K and M, find all pairs of distinct users whose most recent K movies share at least M movies in common. Design an efficient algorithm and analyze its time and space complexity.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

The naive approach is obvious: for each user grab their last K movies as a set, then do pairwise intersections across all users.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then propose an efficient algorithm using inverted indices or minhashing to avoid comparing all user pairs. Analyze time and space complexity, and discuss trade-offs between exact and approximate methods.

Pro tip: Mention that in practice, Netflix might use approximate similarity (e.g., MinHash/LSH) for scalability, but for exact results, inverted indices on movie IDs are effective. Also, highlight the importance of handling ties in recency and ensuring distinct users.

1. Clarify Requirements

Confirm definitions: 'most recent K movies' means the last K entries in each user's history (if history length < K, use all). 'Share at least M movies' means intersection size >= M. Pairs must be distinct users.

2. Choose Data Structures

For each user, extract the set of their last K movies. Build an inverted index mapping each movie to the list of users who watched it in their last K. Alternatively, use a hash-based approach to count common movies per pair.

3. Design Algorithm

Iterate through each movie's user list, and for each pair of users in that list, increment a counter for that pair. After processing all movies, output pairs with counter >= M. Use a hash map to store pair counts efficiently.

4. Analyze Complexity

Time: O(sum over movies of (freq_movie choose 2)) + O(U*K) for preprocessing, where U is number of users. Space: O(number of distinct pairs) for the counter map, plus O(U*K) for the inverted index.

5. Discuss Optimizations and Trade-offs

For large datasets, consider approximate methods like MinHash/LSH to reduce pair comparisons, or prune users with history length < M. Discuss time-space trade-offs and potential parallelization.

Key Points to Mention

  • Handling edge cases: users with fewer than K movies, ties in recency, and ensuring distinct pairs.
  • Using an inverted index to efficiently find candidate pairs without comparing all O(U^2) pairs.
  • Time complexity analysis: focusing on the sum of combinations of user frequencies per movie.
  • Space complexity: storing pair counts and inverted index, and potential memory optimizations.
  • Trade-offs between exact and approximate algorithms (e.g., MinHash/LSH) for scalability.
  • Practical considerations for Netflix-scale data: distributed processing, streaming updates, and caching.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.