How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

What difficulty level is this interview question?

This is a medium difficulty ML System Design question, commonly asked during Technical Screen rounds at Bytedance.

What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at Bytedance during technical interviews.

Design Self-Dealing Detection for Marketplaces | Bytedance Interview Question

Quick Overview

This question evaluates competency in machine learning system design for fraud detection, focusing on modeling buyer–seller–item–transaction relationships, feature engineering across graph, temporal, behavioral and account-linking signals, and production concerns; it is in the ML System Design domain and emphasizes practical application and system-level architecture over purely theoretical concepts. It is commonly asked to assess a candidate’s ability to reason about data collection and labeling, model and rule trade-offs, evaluation metrics, scalability and deployment trade-offs when detecting coordinated or self-dealing activity in marketplace environments.

Design a machine learning system to detect self-dealing or fake transactions in an e-commerce marketplace. In this setting, a seller may use related buyer accounts to purchase their own items in order to inflate sales volume, manipulate rankings, generate fake reviews, or create fraudulent transaction history.

Your design should cover:

What data you would collect.
How you would model buyer, seller, item, and transaction relationships.
What graph, temporal, behavioral, and account-linking features you would use.
Whether you would use rules, classical machine learning, graph embeddings, graph neural networks, or a hybrid approach.
How you would generate candidates and labels.
How you would evaluate the system offline and online.
How you would deploy the model and handle false positives.

Quick Overview

Your design should cover:

What data you would collect.
How you would model buyer, seller, item, and transaction relationships.
What graph, temporal, behavioral, and account-linking features you would use.
Whether you would use rules, classical machine learning, graph embeddings, graph neural networks, or a hybrid approach.
How you would generate candidates and labels.
How you would evaluate the system offline and online.
How you would deploy the model and handle false positives.

Design Self-Dealing Detection for Marketplaces

Quick Overview

Solution

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Design Self-Dealing Detection for Marketplaces

Quick Overview

Solution

Comments (0)