Solving Fashion Recommendation -- The Farfetch Challenge

TL;DR

This paper presents a winning solution to the Farfetch Fashion Recommendation Challenge, using Catboost and Bayesian Optimization to improve click prediction accuracy in an e-commerce setting.

Contribution

The paper introduces a competitive approach combining Catboost and Bayesian Optimization for fashion recommendation, achieving state-of-the-art MRR scores.

Findings

Bayesian optimization improved MRR from 0.5153 to 0.5240.

Final test MRR achieved was 0.5257.

The approach effectively predicts user clicks in fashion recommendations.

Abstract

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.