This question evaluates a data scientist's competency in representation learning and metric learning for entity similarity, feature engineering across structured, unstructured, and graph data, large-scale retrieval architecture (candidate generation and re-ranking), and operational concerns such as scaling, freshness, cold-start handling, negative sampling, and leakage detection. It is commonly asked in Machine Learning and information retrieval/system-design interviews to probe architectural trade-offs, quantitative evaluation (e.g., Recall@K, nDCG, coverage, diversity), and practical system-level design considerations, emphasizing practical application grounded in conceptual understanding.
You are given an anchor client (e.g., The Coca‑Cola Company). Design a system to return the top‑20 most similar companies globally for sales prospecting.
Precisely define similarity across these dimensions and explain how to learn/encode it without many labeled "similar" pairs:
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