Home Depot Product Search Relevance, Winners' Interview: 1st Place Alex, Andreas, & Nurlan

A total of 2,552 players on over 2,000 teams participated in the Home Depot Product Search Relevance competition which ran on Kaggle from January to April 2016. Kagglers were challenged to predict the relevance between pairs of real customer queries and products. In this interview, the first place team describes their winning approach and how computing query centroids helped their solution overcome misspelled and ambiguous search terms. Andreas: I have a PhD in Wireless Network Optimization using statistical and machine learning techniques. I worked for 3.5 years as Senior Data Scientist at AGT International applying machine learning in different types of problems (remote sensing, data fusion, anomaly detection) and I hold an IEEE Certificate of Appreciation for winning first place in a prestigious IEEE contest.