Design a news feed ranking system
PremiumML systemMedium
- Multi task learning
- Feed ranking
- Value model
Quick answer
This ML system interview question asks you to design a news feed ranking system. Aim for a design that turns data into useful predictions in production, then explain the data, training, serving and monitoring. Interviewers want to see how you frame the problem, measure success and keep the model healthy after launch.
The problem
Design a news feed ranking system like Facebook's or LinkedIn's: order the posts from friends, pages and groups so each user sees the most valuable ones first. The hard parts are predicting several kinds of engagement at once, combining them into one score and avoiding a feed that wins clicks today but loses users over months.
Each time a user opens the app, about 1,500 eligible posts from the last 2 days are candidates. The system ranks them and shows them in order as the user scrolls. Users can like, comment, share, hide a post or unfollow its author.
What must a news feed ranking system do?
- Ranked feed: Rank eligible posts for a user and return the first 50 in order.
- Engagement predictions: Predict probabilities of like, comment, share, long dwell and hide for each post.
How well must it do it?
- Latency: First page ready within 300 ms p99, of which 120 ms for ranking.
- Long-term value: Launches must not lower daily active users in a 3-month holdout.
What scale should you design for?
Use these numbers in your estimates.
The scale numbers unlock with this questionUnlock
Key concepts
- Multi task learning
- Feed ranking
- Value model
- Position bias
- Long term holdout
- Integrity signals
How you’ll practise
- Problem framing
- Data and labels
- Features and model
- Training and evaluation
- Serving and monitoring
- Wrap-up
Lala, personalized to you, gives a hint when you ask and reviews your design when you are ready.
What do interviewers look for?
In any ML system design round.
- Frames the product goal as a clear ML problem
- Defines offline and online success metrics
- Explains where the data and labels come from
- Separates training from serving and keeps features consistent
- Plans for drift, retraining and rollbacks
- Weighs model quality against latency and cost
Questions people ask about this interview question
- Which design type is this question?
- It is an ML system question. You design a news feed ranking system and explain your choices the way you would in an interview.
- Is this question asked at entry level?
- It is marked Medium. Entry-level engineers can practise it, but it expects you to reason about requirements and trade-offs, not only list components.
- What should I cover first?
- Start with the product goal, how success is measured and what data is available. Agree on scope with the interviewer before you design anything.
- Can I practise it for free?
- It is a premium question. Sign up and unlock it once for 10 credits; it then stays unlocked. When you finish, Lala, your personalized AI mentor, can review your design for 30 credits.
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Updated · Written by the SevyDevy team