How do you build semantic similarity ranking? What distance metric?

How do you build semantic similarity ranking? What distance metric?medium

Type: scenario
Topic: how-do-you-build-semantic-similarity-ranking-what-distance
Frequency: common
Tags: rag, how, did, you, build, semantic

Answer

Built a domain-specific corpus of 32K CS keywords via Wikipedia API (3 levels deep).

Explanation

Built a domain-specific corpus of 32K CS keywords via Wikipedia API (3 levels deep). Trained Word2Vec (Skip-gram) using Gensim. Represented job descriptions and resumes as averaged word vectors. Ranked candidates by cosine similarity. Cosine was chosen because it measures directional similarity regardless of vector magnitude — better for sparse high-dimensional embedding spaces than Euclidean.

Follow-upWhat tradeoffs did you consider in that implementation?