← Bytedance Interview Insights
Start by clarifying the business objectives and constraints, then outline a data-driven system that combines graph-based anomaly detection with supervised models, and finally discuss trade-offs in precision/recall, latency, and scalability. Emphasize how you would iterate from rule-based heuristics to ML models while incorporating feedback loops.
Pro tip: Frame the problem as a graph anomaly detection task and highlight the importance of temporal dynamics—fraud patterns evolve, so your system must adapt via continuous monitoring and retraining. Also, mention how you'd handle label scarcity by using weak supervision or semi-supervised learning.
Ask questions to understand scale, data available, latency requirements, and what constitutes self-dealing/wash transactions. Define precise metrics (e.g., precision@k, recall) and business impact.
Identify relevant data sources: transaction logs, user profiles, IP addresses, device fingerprints, reviews. Engineer features at user, seller, and graph levels (e.g., shared attributes, transaction velocity, review sentiment).
Propose a hybrid approach: build a heterogeneous graph of users, sellers, transactions, and reviews; apply graph neural networks (GNNs) for node classification or link prediction. Complement with sequence models (LSTM/Transformer) on transaction sequences to capture temporal patterns.
Design offline evaluation using historical labeled data and online A/B testing. Discuss deployment architecture: batch scoring for offline detection and streaming for real-time alerts. Address scalability with distributed training and inference.
Incorporate human-in-the-loop feedback to label false positives/negatives, retrain models periodically, and monitor for concept drift. Suggest active learning to prioritize uncertain cases for review.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.