My first instinct was to jump straight to precision and recall, which was fine, but I kind of glossed over the brand-safety angle for too long.
Start by defining the problem precisely: brand clients searching their own name get irrelevant or harmful results. Then outline a diagnostic framework using metrics like search relevance, harmful content prevalence, and user engagement, followed by a structured approach to root cause analysis and experimentation to fix it.
Pro tip: Emphasize the importance of distinguishing between relevance issues and harmful content issues, as they require different solutions. Also, mention the need to balance brand safety with user experience and platform policies.
Clarify what 'irrelevant' and 'harmful' mean in this context. Establish metrics such as search result relevance (e.g., precision@k, NDCG), harmful content prevalence (e.g., percentage of results flagged as harmful), and brand client satisfaction (e.g., NPS or complaint rate).
Analyze search logs for brand name queries to identify patterns. Segment by query type, user demographics, and content type. Compare metrics against a baseline or control group (e.g., other brand queries or general searches).
Investigate potential causes: algorithmic ranking issues, insufficient brand-specific signals, spam or malicious content targeting the brand, or lack of content moderation. Use techniques like cohort analysis, content audits, and model interpretability.
Based on root causes, suggest interventions such as improving ranking algorithms, adding brand-specific filters, enhancing content moderation, or providing brand control tools. Prioritize by impact and feasibility.
Design A/B tests to measure the effectiveness of chosen solutions. Define success metrics, run experiments, and iterate based on results. Ensure changes don't negatively impact overall search quality or user engagement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.