ML Case-study Interview Question: Boosting Streaming Watch Time Using Collaborative Filtering Recommendations
Case-Study question
A major streaming service seeks to build a system that recommends shows to users based on their viewing habits and preferences. The service has a large user base, a vast content library, and limited real estate on the user’s home screen for recommendations. The goal is to boost total watch time while maintaining user satisfaction. How would you design such a system, and what data and techniques would you use to handle issues like user similarity, cold-start challenges for new shows and new users, and evaluating whether a new recommendation model is worthy of release?
In-depth solution
Data Representation
Represent user preferences with a matrix. Each row corresponds to a user. Each column corresponds to a show. Each matrix cell contains either a rating (for example, 1 to 5) or no value if the user has not watched or rated that show. Rows can be huge in number because the platform has many users. The same applies to columns for shows in the content library. This matrix can be sparse if many users have not watched or rated certain shows.
Collaborative Filtering Approach
Construct recommendations by comparing user rows and identifying similarities. Similar users often enjoy the same shows. Use these similarities to predict how a user might rate shows they have not yet watched. Recommend the top predicted shows to the user.
Core Similarity Formula
Here, u is the rating vector of the first user. v is the rating vector of the second user. u dot v is the dot product of both users' rating vectors. ||u|| is the Euclidean norm (magnitude) of the first user's ratings vector. ||v|| is the Euclidean norm of the second user's ratings vector. The result ranges from -1 to 1, though ratings are typically nonnegative, so similarity often remains within [0,1]. Normalization of ratings can help remove user-specific biases in rating scales.
Recommendation Logic
Score unwatched shows for a user by averaging ratings from other users who share high similarity scores with that user. Weight these averages by the similarity level. Rank these candidate shows and recommend the top ones. If a new show has no ratings yet, incorporate additional features (genre, release date, typical watch patterns among a small pilot group) or rely on general popularity metrics.
Cold-Start Solutions
Use popularity-based recommendations or trending shows for new users until enough ratings or watch history is collected. For new shows, present them in “new releases” sections or rely on content-based features such as genre, cast, or language to place them in front of potentially interested users.
Alternative Methods
Employ content-based filtering, which focuses on similarities among shows’ attributes, rather than user ratings alone. Combine both collaborative filtering and content-based approaches for a hybrid system that can better handle sparse data scenarios or newly added shows.
A/B Testing and Launch Decisions
Run controlled experiments comparing a new recommendation model to the existing system. Track watch time, retention, and user engagement. Compare the results with appropriate statistical tests to confirm improvements are not due to chance. Check counter metrics such as average ratings of recommended shows to ensure the system does not push content users end up disliking.
Model Deployment and Product Considerations
Evaluate the training cost of the system, especially if it uses matrix factorization or requires large-scale similarity calculations. Decide how often to retrain to keep pace with shifting user behavior and new content. Account for editorial decisions, such as whether to boost original content. Update recommendations dynamically if a user’s watch history quickly changes. Ensure a robust infrastructure for near-real-time or batch updates depending on latency requirements.
How do we measure user similarity?
Compute similarity by comparing each user’s rating vector to another user’s vector. Normalize rating vectors to remove a user’s mean rating bias. Calculate the cosine similarity or Pearson correlation. If many users have large rating variations, or some are strict while others are lenient, normalization is critical. Similarity scores help cluster users with like-minded preferences.
How do we handle the cold-start problem for new shows and new users?
Present new shows in separate panels labeled as fresh or trending. For new users, show generally popular shows until their watch history is available. For new shows, consider metadata such as genre, cast, or language. Use content-based features to align new items with existing shows liked by similar user segments. Once the new user has made enough interactions, switch to collaborative filtering.
If a new recommendation model increases total watch time with a p-value of 0.04, should we ship?
Check that the A/B test ran long enough to capture user behavior cycles (weekdays vs weekends). Confirm that the sample size is large enough to detect meaningful differences. Examine counter metrics such as average user rating to ensure the model is not driving engagement at the cost of lower satisfaction. Consult stakeholders who track business objectives to see if the improvement is practically significant. If watch time is up but user satisfaction remains steady or improves, the new model is a good candidate for release.
What counter metrics should be considered?
Track the proportion of recommended shows that users actually watch (precision). Check of the shows the user watched, how many were recommended (recall). Observe average ratings or user happiness after watching recommended shows. Monitor churn or retention to see if recommendations keep users returning or drive them away. Ensure the system does not funnel viewers into narrow content silos.
What other product or deployment considerations matter?
Ensure the system scales efficiently for massive user and item sets. Consider partial updates or online learning to keep recommendations fresh. Decide if editorial curation is required for promotions or brand-aligned shows. Use hierarchical or segment-based approaches if some users exhibit special patterns. Evaluate how to present recommended shows to users in the user interface for maximum clarity and minimal confusion. Adapt retraining frequency to balance resource costs and accuracy improvements.