Design a personalized recommendation system

Quick Overview

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.

Design a personalized recommendation system

Company: Disney

Role: Software Engineer

Category: ML System Design

Difficulty: hard

Interview Round: Technical Screen

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.

Overview: 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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Sep 6, 2025
hardSoftware EngineerTechnical ScreenML System Design
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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:

  1. 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.
  2. 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.
  3. 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.
  4. Evaluation and Experimentation
    • Offline metrics and validation.
    • Online metrics, guardrails, and an A/B testing plan (power, duration, ramping, and guardrails).
  5. 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.

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