I started talking about string similarity on restaurant names and got about two sentences in before realizing that's nowhere near enough.
Start by clarifying requirements: what defines a duplicate, acceptable false positive/negative rates, and scale. Then propose a multi-stage pipeline: candidate generation using blocking/indexing, followed by pairwise similarity scoring with fuzzy matching and ML, and finally a decision layer with human review for ambiguous cases. Discuss trade-offs between precision and recall, and how to handle feedback loops.
Pro tip: Emphasize that perfect deduplication is impossible; instead, design a system that surfaces likely duplicates with confidence scores and routes ambiguous cases to human review, while continuously learning from feedback to improve thresholds.
Ask about data sources, volume, latency needs, and what constitutes a duplicate (exact match vs. fuzzy). Establish acceptable precision/recall and cost of errors.
Use blocking or indexing (e.g., on name, address, phone) to reduce the search space. Consider phonetic algorithms, geohashing, or inverted indexes to generate candidate pairs efficiently.
Apply string similarity metrics (Jaro-Winkler, Levenshtein) and feature-based ML models to score candidate pairs. Combine multiple signals (name, address, cuisine) into a single match probability.
Set thresholds for auto-merge, auto-reject, and manual review. For ambiguous cases, queue for human review and use active learning to improve the model.
Measure system performance with precision/recall on labeled data. Monitor production and incorporate feedback to adjust thresholds and retrain models.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.