Clarify the definition of 'similar' by discussing possible metrics such as lexical overlap, semantic similarity, or edit distance. Then propose a practical algorithm, e.g., using word embeddings and cosine similarity, and analyze its time and space complexity. Be prepared to discuss trade-offs and potential improvements.
Pro tip: Show awareness of real-world constraints: mention that exact string matching is insufficient for semantic similarity, and that scalable solutions often require approximate nearest neighbor search or pre-trained models. Also, ask clarifying questions about the expected scale and latency requirements.
Ask the interviewer to define 'similar' (e.g., lexical, semantic, syntactic) and discuss constraints like input size, language, and performance needs.
Select an appropriate metric such as Jaccard similarity, cosine similarity of embeddings, or edit distance, based on the clarified definition.
Outline steps: preprocess sentences (tokenize, normalize), compute representations (e.g., TF-IDF, word embeddings), and calculate similarity score.
Discuss time and space complexity, and propose optimizations like caching, indexing, or using approximate methods for large-scale data.
Suggest test cases (identical, completely different, paraphrases) and evaluation metrics (accuracy, precision/recall) to validate the approach.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.