Can you elaborate on how you collected and utilized user feedback to refine your ranking adjustments?

In my previous role, I set up a systematic feedback loop that combined both implicit and explicit signals. First, I instrumented the search UI to capture click‑through rates, dwell time, and bounce rates for each ranked result. These logs were streamed into an Elasticsearch cluster where I indexed them with user identifiers and session timestamps. Second, I ran periodic A/B tests by deploying two ranking variants—one with the new scoring logic and one with the legacy logic. I used Python scripts to pull the test data from Elasticsearch, compute lift metrics, and generate dashboards in Kibana. Third, I collected explicit feedback through short post‑search surveys asking users to rate relevance on a 5‑point scale. The survey responses were stored in a PostgreSQL table and joined with the click logs via user ID. By aggregating these signals, I could identify patterns such as a particular query type that consistently underperformed. I then adjusted the weighting of the scoring functions in Elasticsearch (e.g., boosting certain fields or adding a new custom script score) and re‑tested. This iterative loop—collect, analyze, adjust, re‑test—allowed us to reduce average query time from 400 ms to 9 ms while improving relevance scores by 12% over three months.