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This is a hard difficulty Machine Learning question, commonly asked during Onsite rounds at Yelp.

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This question is commonly asked for Data Scientist candidates at Yelp during technical interviews.

Design a Cold-Start-Aware Recommender | Yelp Interview Question

Quick Overview

This question evaluates a data scientist's expertise in production recommender systems, covering cold-start strategies, two-stage retrieval and ranking model design, multimodal feature engineering, exploration–exploitation, bias-corrected experimentation, and low-latency serving.

System Design: Two-Stage Recommender for a New Content App

Context

You are designing recommendations for a new content app with sparse interactions and frequent new items. The system must be robust to user and item cold-start, and meet strict latency SLAs. You will propose a two-stage recommendation system, define models, losses, sampling, features, exploration strategy, metrics, and an A/B testing plan that addresses selection bias. Finally, you will specify how to meet end-to-end latency targets.

Requirements

Two-stage architecture:
- Stage 1 (Candidate Generation/Retrieval): propose concrete model(s) (e.g., matrix factorization, two-tower retrieval), loss function(s), negative sampling strategy, and how to handle user/item cold-start.
- Stage 2 (Ranking): propose concrete model(s) (e.g., gradient-boosted trees, DNN ranker), loss function(s), negative sampling strategy, and key features.
Features: list the key user, item, and context features used in both stages, including any multimodal content features (text, image) relevant for new items.
Exploration vs. exploitation: specify the strategy (e.g., Thompson sampling, epsilon-greedy) and how you will cap propensities to control variance.
Evaluation:
- Offline metrics (by stage).
- Online KPIs.
- A/B test design that controls for selection bias using IPS/SNIPS or doubly robust estimators. Write the estimator you would compute.
- Guardrails for long-term outcomes.
Latency and serving:
- Show how to meet p50 < 50 ms and p99 < 200 ms end-to-end latency.
- Include feature store access patterns, caching, ANN retrieval, and fallback strategies.

Quick Overview

Context

Requirements

Two-stage architecture:

Stage 1 (Candidate Generation/Retrieval): propose concrete model(s) (e.g., matrix factorization, two-tower retrieval), loss function(s), negative sampling strategy, and how to handle user/item cold-start.
Stage 2 (Ranking): propose concrete model(s) (e.g., gradient-boosted trees, DNN ranker), loss function(s), negative sampling strategy, and key features.

Features: list the key user, item, and context features used in both stages, including any multimodal content features (text, image) relevant for new items.

Exploration vs. exploitation: specify the strategy (e.g., Thompson sampling, epsilon-greedy) and how you will cap propensities to control variance.

Evaluation:

Offline metrics (by stage).
Online KPIs.
A/B test design that controls for selection bias using IPS/SNIPS or doubly robust estimators. Write the estimator you would compute.
Guardrails for long-term outcomes.

Latency and serving:

Show how to meet p50 < 50 ms and p99 < 200 ms end-to-end latency.
Include feature store access patterns, caching, ANN retrieval, and fallback strategies.

Design a Cold-Start-Aware Recommender

Quick Overview

System Design: Two-Stage Recommender for a New Content App

Context

Requirements

Solution

Submit Your Answer to Earn 20XP

Design a Cold-Start-Aware Recommender

Quick Overview

System Design: Two-Stage Recommender for a New Content App

Context

Requirements

Solution

Submit Your Answer to Earn 20XP