Design an ML keyword recommendation pipeline

Q: Design an ML keyword recommendation pipeline

This is a ML System Design interview question from Apple for Machine Learning Engineer roles. View the full question and solution on PracHub.

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Question

ML System Design: Typed Search Keyword Recommendations for an App Marketplace

Goal

Design an end-to-end ML pipeline that, given a user query (e.g., "games"), generates diverse, typed keyword suggestions (e.g., "puzzle games", "RPG games", "racing games") with high relevance and coverage.

Assume you are designing for a large-scale app marketplace with millions of users and tens of thousands of queries per second during peak. Typed suggestions are grounded in a controlled taxonomy (e.g., Genre, Feature, Price, Age, Mode) and must be compliant with marketplace policies.

Requirements

Objectives and constraints

Relevance, diversity, coverage
Freshness/trends, multilingual/locale correctness
Latency and availability SLOs
Privacy and policy compliance

Data sources and labeling/feedback

Query/search logs, clicks, installs, uninstalls
App metadata and taxonomy
Reviews text, co-search/co-click graphs
Embeddings, locale signals
Labeling: implicit feedback (CTR/installs), counterfactual debiasing, editorial seeds

System architecture

Candidate generation: lexical, semantic (ANN), taxonomy, graph/co-occurrence, trending
Ranking: multi-stage (LTR + neural), diversity-aware re-rank
Feature store (offline/online), offline training, online serving, cache, retrieval indices

Features

Text/semantic embeddings, lexical features
Popularity/recency/trending signals
User/context signals (locale, device)
Co-occurrence/graph features (PMI, P(s|q))
Quality/spam trust signals

Models and choices

Retrieval: BM25, two-tower ANN, graph-based expansion
Ranking: GBDT, pairwise/listwise LTR, cross-encoder re-ranker, optional sequence/graph models

Evaluation and experimentation

Metrics: CTR, install rate, NDCG, recall@K, coverage/diversity, latency/errors, calibration
A/B testing with guardrails and statistical rigor

Continual training/ops

Streaming feedback ingestion, feature freshness, update cadence
Warm-starting, drift detection, rollback

Special cases

Cold start, multilingual/locale variants, spam/abuse, fairness and policy

Design an ML keyword recommendation pipeline

ML System Design: Typed Search Keyword Recommendations for an App Marketplace

Goal

Requirements

Solution (Locked)

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