Context-aware Retail Product Recommendation with Regularized Gradient Boosting

TL;DR

This paper presents a context-aware product recommendation system using regularized gradient boosting, achieving top-tier results in a large-scale fashion recommendation challenge by incorporating user context and similarity measures.

Contribution

The authors introduce a novel context-aware ranking approach with regularized gradient boosting, significantly improving recommendation accuracy in a real-world fashion dataset.

Findings

Achieved 6th place with MRR of 0.4658 in the challenge.

Fine-tuned approach reached an MRR of 0.4784, potentially placing 3rd.

Demonstrated effectiveness of context-aware features in product ranking.

Abstract

In the FARFETCH Fashion Recommendation challenge, the participants needed to predict the order in which various products would be shown to a user in a recommendation impression. The data was provided in two phases - a validation phase and a test phase. The validation phase had a labelled training set that contained a binary column indicating whether a product has been clicked or not. The dataset comprises over 5,000,000 recommendation events, 450,000 products and 230,000 unique users. It represents real, unbiased, but anonymised, interactions of actual users of the FARFETCH platform. The final evaluation was done according to the performance in the second phase. A total of 167 participants participated in the challenge, and we secured the 6th rank during the final evaluation with an MRR of 0.4658 on the test set. We have designed a unique context-aware system that takes the similarity of a product to the user context into account to rank products more effectively. Post evaluation, we have been able to fine-tune our approach with an MRR of 0.4784 on the test set, which would have placed us at the 3rd position.