  Recommendation engines are integral to the modern e-commerce experience, both
for the seller and the end user. Accurate recommendations lead to higher
revenue and better user experience. In this paper, we are presenting our
solution to ECML PKDD Farfetch Fashion Recommendation Challenge. The goal of
this challenge is to maximize the chances of a click when the users are
presented with set of fashion items. We have approached this problem as a
binary classification problem. Our winning solution utilizes Catboost as the
classifier and Bayesian Optimization for hyper parameter tuning. Our baseline
model achieved MRR of 0.5153 on the validation set. Bayesian optimization of
hyper parameters improved the MRR to 0.5240 on the validation set. Our final
submission on the test set achieved a MRR of 0.5257.
