Tailor: Size Recommendations for High-End Fashion Marketplaces

Autor:	Candeias, Alexandre, Silva, Ivo, Sousa, Vitor, Marcelino, José
Rok vydání:	2024
Předmět:	Computer Science - Information Retrieval Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted in FashionXRecsys23 held at the 17th ACM Conference on Recommender Systems, 18th-22nd September 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.01978 Zobrazit plný text záznamu View this record from Arxiv