Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems
DOI:
https://doi.org/10.32473/flairs.36.133307Keywords:
Multi-stage recommender systems, Fashion recommendation, Learning to RankAbstract
Fashion industry is driven by fashion cycles, in which a fashion item is launched, rises to mainstream appeal and becomes a trend, then diminishes and eventually becomes obsolete. These properties make it critical to incorporate temporal information when adapting a recommendation framework to be employed in the fashion domain. However, an industry standard real-world recommendation architecture entails numerous phases, including data preparation, establishing and training recommender models, filtering and fulfilling revenue-based user needs. The contributions of the presented work are twofold. We first formalise the multi-stage recommendation pipeline by including the time dimension intrinsically present in the fashion data. We then present a study to incorporate explicit fashion domain characteristics into the presented pipeline. Finally, we conduct comprehensive experimentation on a real-world web-scale fashion dataset released by H\&M, illustrating how including domain knowledge in the multi-stage framework can lead to significantly improvement on the final recommendation performance.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Aayush Singha Roy, Edoardo D'Amico, Aonghus Lawlor, Neil Hurley

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.