Solving Fashion Recommendation -- The Farfetch Challenge
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimization
Inverse Scene Text Removal
Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control
Fast Bayesian Optimization of Function Networks with Partial Evaluations
EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements
Amortized variational transdimensional inference
토론
이 논문과 연결된 포럼 글이 아직 없습니다.