Design a personalized recommendation system

Q: Design a personalized recommendation system

This question evaluates a candidate's competency in ML system design and recommender systems engineering, covering data ingestion, feature stores, candidate generation, ranking, online serving, experimentation, and operational considerations.

Q: How do I approach ML System Design interview questions?

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

Q: What difficulty level is this interview question?

This is a hard difficulty ML System Design question, commonly asked during Technical Screen rounds at Disney.

Q: What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Disney during technical interviews.

Question

System Design: Personalized Recommendations for a Consumer App

Context

Assume you are building the home-feed recommendations for a large consumer app (choose one: video streaming, news, or e-commerce). For concreteness, you may anchor your design on a video streaming app with both subscription and ad-supported content. You must propose a production-ready system that balances user experience with engineering constraints.

Requirements

Design an end-to-end personalized recommendation system and address the following:

Objectives and Constraints
- Clarify business objectives (e.g., engagement, retention, revenue) and trade-offs (engagement vs. monetization).
- Define latency SLAs (p95/p99) and freshness requirements for new content and user signals.
Architecture (end-to-end)
- Data ingestion and logging, streaming vs. batch.
- Feature store (offline and online parity).
- Candidate generation (recall) strategies and services.
- Ranking model(s) and serving.
- Re-ranking for diversity, deduplication, and business rules.
- Online inference path and caching.
- Feedback loops for model updates and real-time features.
Modeling and Algorithms
- Specify models for candidate generation (e.g., two-tower, collaborative filtering, content-based, graph/sequence models) and ranking (e.g., GBDT/DNN/MTL).
- Handle cold-start for users/items and exploration (e.g., contextual bandits).
- Real-time updates, deduplication, diversity, and content safety.
Evaluation and Experimentation
- Offline metrics and validation.
- Online metrics, guardrails, and an A/B testing plan (power, duration, ramping, and guardrails).
Scale and Reliability
- Traffic assumptions, QPS estimates, storage sizing, and sharding.
- Caching strategy, failure modes, and graceful degradation.
- Monitoring/alerting (SLOs), privacy, and bias/fairness considerations.

Deliverable

Provide a structured proposal with diagrams-as-text or clear component breakdowns, explicit assumptions, and concrete numbers where helpful (e.g., latency budgets, QPS, storage). Include alternatives and trade-offs where relevant.

Design a personalized recommendation system

Quick Overview

System Design: Personalized Recommendations for a Consumer App

Context

Requirements

Deliverable

Solution

Comments (0)