Design Comprehensive Recommendation System for Spokeo Features

Q: Design Comprehensive Recommendation System for Spokeo Features

This question evaluates the ability to design an end-to-end recommendation system for a people-search platform, testing competencies in data collection (batch and streaming), feature engineering, candidate generation and ranking, offline and online evaluation metrics, real-time serving architecture, experimentation, and cold-start handling.

Q: How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

Question

Design an End-to-End Recommendation System for Spokeo

Scenario

You are designing a new recommendation system for Spokeo (a people-search platform) to help users find relevant profiles and related searches more quickly and safely.

Task

Propose an end-to-end design that covers:

Data collection and pipeline (batch + streaming)
Feature engineering
Model selection (candidate generation + ranking)
Evaluation metrics (offline and online)
Real-time serving architecture
A/B testing and experimentation plan

Include how you will handle cold-start users/items, and specify both offline and online metrics.

Assumptions

Primary recommendation surfaces: (a) search results and (b) profile detail pages.
Target recommendations: "profiles you might be looking for" and "related searches".
Objectives: improve relevance (find the right person), efficiency (fewer reformulations), and safety (avoid harmful/sensitive suggestions).

Design Comprehensive Recommendation System for Spokeo Features

Design an End-to-End Recommendation System for Spokeo

Scenario

Task

Assumptions

Solution

Comments (0)