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Implement bag-of-words similarity search from scratch

AppleMachine Learning Engineer · Technical Screen
MediumMachine Learning EngineerTechnical ScreenCoding & Algorithms

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This question evaluates knowledge and implementation skills in text processing, vector-space models, and information retrieval, covering tokenization, term-frequency and TF–IDF weighting, cosine similarity, and ranking for similarity search within the Coding & Algorithms domain.

Implement a bag-of-words–based text similarity search engine from scratch. Write code that: (

  1. tokenizes text (lowercasing, punctuation handling, Unicode support, and optional stopword removal/stemming—justify your choices), (
  2. builds document vectors using term frequency and supports TF–IDF weighting, (
  3. computes similarity scores (implement cosine similarity; optionally compare with Jaccard), and (
  4. returns the top-k most similar document IDs for a given query along with their scores. Clearly define each function’s purpose and inputs/outputs, and provide a short example demonstrating end-to-end usage. Analyze time and space complexity for indexing and querying, and briefly discuss how you would scale to large corpora (e.g., inverted index, pruning, or approximate search).