Solving Fashion Recommendation -- The Farfetch Challenge
This work addresses the problem of improving recommendation accuracy for e-commerce fashion platforms, but it is incremental as it applies existing methods to a specific challenge.
The paper tackled the Farfetch Fashion Recommendation Challenge by developing a solution to maximize click probabilities for fashion items, achieving a final test set MRR of 0.5257.
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.