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Design a game recommendation modeling approach

RobloxSoftware Engineer · Technical Screen
EasySoftware EngineerTechnical ScreenML System Design

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This problem tests the ability to design an end-to-end recommendation engine for a massive-scale gaming platform, focusing on user-item interaction modeling and handling cold-start scenarios.

Problem: Designing a Game Recommendation System for Roblox

You are tasked with designing a game recommendation system for a platform with millions of active users and millions of user-generated experiences (games). The goal is to provide a 'Recommended for You' feed on the homepage.

Requirements

  1. Define the Objective: What metrics will you optimize, and how do you translate user engagement into a training objective?
  2. Feature Engineering: What user, item, and contextual features are essential? How do you handle the high churn rate of new games (the 'cold-start' problem)?
  3. Candidate Generation & Ranking: Propose a two-stage architecture (retrieval and ranking) to scale to millions of items.
  4. Data Handling: Describe your approach to training data construction and how you would prevent data leakage (e.g., preventing future information from influencing past predictions).
  5. Evaluation: How would you validate the system offline (before deployment) and online (A/B testing)?

Constraints

  • The system must provide low-latency recommendations (e.g., < 200ms per request).
  • The platform has a mix of diverse game genres, ranging from social hangouts to high-action competitive games.
  • User behavior is highly dynamic; preferences change rapidly.

Hints

Hint 1

Consider framing the objective as a multi-task learning problem where you predict both clicks and session duration.

Hint 2

Think about using embedding-based retrieval (e.g., Approximate Nearest Neighbors) to handle the scale, followed by a more complex deep learning model for reranking.

Hint 3

For the cold-start problem, utilize item metadata (tags, creators, description) and early performance signals like 'click-through rate among new users' to bootstrap recommendations.

Follow-up questions

  • How would you handle bias in your training data, such as the popularity bias where top games are clicked more frequently just because they are more visible?
  • If you notice that users are clicking on games but spending very little time in them (low-quality engagement), how would you adjust your reward function?
  • How would you redesign the system if you wanted to introduce 'exploration' to show users games they haven't explicitly signaled an interest in yet?