Tailor: Size Recommendations for High-End Fashion Marketplaces

TL;DR

This paper introduces a novel sequence classification method for personalized size recommendations in high-end fashion marketplaces, combining implicit and explicit user signals to improve accuracy and coverage.

Contribution

It presents a new approach integrating LSTM and Attention models with user signals, achieving significant accuracy and coverage improvements over existing methods.

Findings

Model outperforms SFNet with 45.7% accuracy increase.

Using Add2Bag interactions increases user coverage by 24.5%.

Models are evaluated for real-time latency performance.

Abstract

In the ever-changing and dynamic realm of high-end fashion marketplaces, providing accurate and personalized size recommendations has become a critical aspect. Meeting customer expectations in this regard is not only crucial for ensuring their satisfaction but also plays a pivotal role in driving customer retention, which is a key metric for the success of any fashion retailer. We propose a novel sequence classification approach to address this problem, integrating implicit (Add2Bag) and explicit (ReturnReason) user signals. Our approach comprises two distinct models: one employs LSTMs to encode the user signals, while the other leverages an Attention mechanism. Our best model outperforms SFNet, improving accuracy by 45.7%. By using Add2Bag interactions we increase the user coverage by 24.5% when compared with only using Orders. Moreover, we evaluate the models' usability in real-time recommendation scenarios by conducting experiments to measure their latency performance.