Improve iOS App Store Search Relevancy

To improve App Store search relevancy, we must shift from a volume-based ranking model to a quality-and-engagement-first approach. Currently, developers game the system by stuffing keywords or buying fake installs. My strategy involves three pillars. First, we enhance our signal processing by incorporating post-install behavior. Instead of just counting downloads, we weight results higher for apps with high retention rates after day seven and low crash frequencies, ensuring users find stable, useful software. Second, we implement a robust anomaly detection layer using machine learning to identify artificial inflation patterns, such as rapid install spikes from non-organic sources or suspicious review bursts. If detected, we temporarily demote those apps or flag them for manual review. Third, we introduce a transparency metric where apps with clear privacy labels and frequent, meaningful updates get a relevance boost. This aligns with Apple's commitment to privacy and quality. Finally, we would run controlled A/B tests to measure the impact on search-to-download conversion and developer churn. By prioritizing genuine user satisfaction over raw volume, we restore trust in the store and create a healthier ecosystem for everyone.