Design a personalized recommendation system for a consumer app (e.g., video, news, or e-commerce). Clarify objectives and constraints (engagement vs. revenue, latency SLA, freshness). Propose an end-to-end architecture covering data ingestion, feature store, candidate generation, ranking, re-ranking, online inference, and model feedback loops. Specify models/algorithms at each stage, handling cold-start and exploration (e.g., bandits), real-time updates, deduplication/diversity, and content safety. Define offline/online evaluation metrics and an A/B testing plan, including guardrails. Address scalability (traffic estimates, QPS, storage), caching, failure modes, monitoring, privacy, and bias/fairness considerations.

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.

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

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

Design a personalized recommendation system

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.

Context

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.

Design a personalized recommendation system

Quick Overview

System Design: Personalized Recommendations for a Consumer App

Context

Requirements

Deliverable

Solution

Submit Your Answer

Design a personalized recommendation system

Quick Overview

System Design: Personalized Recommendations for a Consumer App

Context

Requirements

Deliverable

Solution

Submit Your Answer