False positives in search occur when irrelevant results incorrectly match a query, often due to overly broad pattern matching or ambiguous terms. They differ from false negatives, where genuinely relevant results are missed. Reducing them involves refining how the search system interprets and processes queries and data, ensuring results align closely with the user's actual intent without missing important matches.

In e-commerce, optimizing product search to distinguish between specific brands (e.g., searching "Apple" returning tech products instead of fruit) involves filters, synonyms, and keyword boosting. Enterprise document systems reduce false positives by implementing context-aware algorithms; for instance, searching "HR policy" prioritizes internal handbooks over unrelated departmental meeting notes using metadata tagging.

Key advantages include improved user trust, efficiency, and higher conversion rates. A limitation is the potential trade-off with recall (missing some relevant results if filters are too strict). Ethically, transparent filtering avoids unintentional bias or censorship. Future developments leverage AI and contextual embeddings for smarter, intent-based matching, driving adoption in platforms needing precise information retrieval.