How a Python-Typesense Pipeline Turns Click Logs into Offline Search Popularity Scores

A developer building an Intelligent Search Platform has detailed an offline popularity-training pipeline that uses Python, PostgreSQL, scikit-learn, and Typesense to improve search ranking without running machine learning models at query time. Instead of a fixed formula, the system fits a logistic regression model on browse events and document features, then converts predictions into bounded popularity scores written back to the search index. The pipeline handles catalog-specific signals by building schema-independent features, applying position-based discounting to click data, and normalizing output scores to a 0–1,000,000 range. Training is triggered on demand via an administrative API endpoint, keeping scheduling logic outside the core search service. The current implementation focuses on browse traffic with empty queries to avoid conflating general document popularity with query-specific relevance.