This document summarizes the key aspects of winning the Kaggle/Yandex competition to re-rank search results according to personal user preferences. It describes the goal of predicting user pertinence for URLs to improve search rankings. It then outlines the team's approach, which involved constructing many features, using Dataiku Science Studio for modeling, and optimizing models like Random Forest and LambdaMART (which won) to directly improve the NDCG ranking metric. The team worked collaboratively over 9 months to achieve the top ranking.