Design hashtag recommender with cold start
Overview
This question evaluates expertise in recommender-system design, feature engineering, ranking and learning-to-rank models, cold-start strategies, evaluation and debiasing techniques, and operational concerns like latency, memory, interpretability, and safety guardrails.
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Oct 13, 2025, 9:49 PM
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Machine Learning
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Hashtag Recommendation Design (Short-Video App)
Task
Design a system to recommend hashtags a user is likely to follow. Answer all parts precisely.
- Features/Signals
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Enumerate at least 12 concrete features used to predict hashtag follow. For each feature, specify:
- Data type: binary, categorical, or continuous
- Time horizon: short-term vs. long-term
- Normalization/bucketing strategy
- Include the following classes of signals: recency-weighted views/likes/comments/saves, negative feedback, creator/user similarity, follow-graph features, session context (time of day, device), geographic and demographic signals, hashtag global/trending velocity, and safety indicators.
- System Architecture
- Propose an end-to-end system with candidate generation and ranking.
- For ranking, compare: linear model, gradient-boosted trees, and wide-and-deep. Pick one and justify with latency (<50 ms P95 per request), memory, interpretability, and cold-start constraints.
- Cold Start
- For brand-new users and unseen hashtags, detail your approach (e.g., trending defaults stratified by region/gender, exploration via epsilon-greedy or Thompson sampling). Provide concrete parameter choices (e.g., epsilon values, priors).
- Learning the Weights
- Define the objective (e.g., cross-entropy or pairwise NDCG), regularization, debiasing for position/propensity, and calibration of scores to follow probability.
- Metrics and Safety
- Specify offline metrics (e.g., NDCG@k, MAP, calibration error).
- Specify online guardrails that ensure violating/sensitive hashtags are never surfaced.
Solution
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