Design hashtag recommender with cold start

Q: Design hashtag recommender with cold start

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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Q: What difficulty level is this interview question?

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

Q: What role is this question designed for?

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

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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

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.

Design hashtag recommender with cold start

Quick Overview

Hashtag Recommendation Design (Short-Video App)

Task

Solution

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