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

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

Design Comprehensive Recommendation System for Spokeo Features

Last updated: Mar 29, 2026

Quick Overview

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.

Spokeo

Aug 4, 2025, 10:55 AM

Data Scientist

Onsite

Machine Learning

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).

Solution

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