How do I approach System Design interview questions?

System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master system design interviews.

What difficulty level is this interview question?

This is a hard difficulty System Design question, commonly asked during Onsite rounds at Meta.

What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at Meta during technical interviews.

Design a recommendation system | Meta Interview Question

Quick Overview

This question evaluates competence in designing large-scale recommendation systems and ML engineering skills such as candidate generation, feature store design (offline/online and point-in-time correctness), real-time signal ingestion, ranking and re-ranking, online exploration, cold-start strategies, feedback-bias mitigation, experimentation, and operational reliability. It is commonly asked to assess the ability to make architecture-level trade-offs for scalability, latency and throughput targets, multi-objective optimization, data governance and outage fallback behavior; it falls under the System Design and Machine Learning domain and requires practical, architecture-level application with conceptual trade-off reasoning rather than low-level implementation detail.

System Design: Large-Scale Home-Feed Recommendation System

Problem

Design a large-scale recommendation system for a consumer app's home feed. Describe the end-to-end architecture and address the following topics:

Core architecture
- Candidate generation
- Feature stores (offline/online) and point-in-time correctness
- Real-time signals ingestion
- Ranking and re-ranking
- Online exploration (e.g., multi-armed bandits)
Strategies
- Cold start for new users and new items
- Feedback loops and bias mitigation
Experimentation and operations
- A/B testing approach
- Latency and throughput targets
- Data privacy and governance
- Fallback behavior during outages

Assumptions (to scope the design)

Consumer social app with hundreds of millions of MAU, peak 100–200k QPS feed requests.
Each request returns ~20–40 items, drawn from a few thousand candidates.
Multi-objective goals: short-term engagement (CTR, dwell, watch time) and long-term value (retention, creator health, diversity, safety).

Quick Overview

Problem

Design a large-scale recommendation system for a consumer app's home feed. Describe the end-to-end architecture and address the following topics:

Core architecture

Candidate generation
Feature stores (offline/online) and point-in-time correctness
Real-time signals ingestion
Ranking and re-ranking
Online exploration (e.g., multi-armed bandits)

Strategies

Cold start for new users and new items
Feedback loops and bias mitigation

Experimentation and operations

A/B testing approach
Latency and throughput targets
Data privacy and governance
Fallback behavior during outages

Assumptions (to scope the design)

Consumer social app with hundreds of millions of MAU, peak 100–200k QPS feed requests.

Each request returns ~20–40 items, drawn from a few thousand candidates.

Multi-objective goals: short-term engagement (CTR, dwell, watch time) and long-term value (retention, creator health, diversity, safety).

Design a recommendation system

Quick Overview

System Design: Large-Scale Home-Feed Recommendation System

Problem

Assumptions (to scope the design)

Solution

Comments (0)

Design a recommendation system

Quick Overview

System Design: Large-Scale Home-Feed Recommendation System

Problem

Assumptions (to scope the design)

Solution

Comments (0)