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Recommendation engines are integral to the modern e-commerce experience, bothfor the seller and the end user. Accurate recommendations lead to higherrevenue and better user experience. In this paper, we are presenting oursolution to ECML PKDD Farfetch Fashion Recommendation Challenge. The goal ofthis challenge is to maximize the chances of a click when the users arepresented with set of fashion items. We have approached this problem as abinary classification problem. Our winning solution utilizes Catboost as theclassifier and Bayesian Optimization for hyper parameter tuning. Our baselinemodel achieved MRR of 0.5153 on the validation set. Bayesian optimization ofhyper parameters improved the MRR to 0.5240 on the validation set. Our finalsubmission on the test set achieved a MRR of 0.5257.

Solving Fashion Recommendation -- The Farfetch Challenge