Find linked user records by weighted similarity

You are given a list of user records. Each record has fields: - `id` (unique) - `name` - `email` - `company` You are also given: - `weights`: a map from field name to weight (e.g., `name: 0.2, email: 0.5, company: 0.3`) - `threshold`: a float - `target_user_id` ## Similarity scoring Define `similarity(recordA, recordB)` as the sum over fields of: - `weights[field] * field_similarity(field_valueA, field_valueB)` where `field_similarity` returns a value in `[0,1]` (the exact function is provided/assumed in the interview; for example, exact match => 1, otherwise 0; or a string similarity). Two records are considered **linked** if their total similarity score is `>= threshold`. ## Task Return all record IDs that should be considered the same user as `target_user_id`. ### Follow-up 1: include 1-hop indirect links Include not only records directly linked to the target, but also records linked to those direct matches (i.e., within 2 steps from the target), even if they are not directly linked to the target. ### Follow-up 2: include all indirect links (connected component) Return all record IDs in the entire connected component containing `target_user_id`, where edges connect pairs of records whose similarity is `>= threshold`. ## Notes - Clarify whether the output includes the target ID itself. - Aim for an approach that avoids unnecessary pairwise comparisons when possible (discuss indexing/blocking if relevant).