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.

翻译：在高端时尚市场这一瞬息万变且充满活力的领域中，提供准确且个性化的尺码推荐已成为关键环节。满足客户在此方面的期望，不仅对确保其满意度至关重要，还在驱动客户留存方面发挥着核心作用，而客户留存是衡量任何时尚零售商成功与否的关键指标。我们提出了一种新颖的序列分类方法来解决这一问题，该方法整合了隐式（Add2Bag）和显式（ReturnReason）用户信号。我们的方法包含两个不同的模型：一个使用LSTM对用户信号进行编码，另一个则利用注意力机制。我们最优模型的性能比SFNet提升了45.7%的准确率。通过使用Add2Bag交互，与仅使用订单数据相比，我们将用户覆盖率提高了24.5%。此外，我们通过测量模型的延迟性能来评估其在实时推荐场景中的可用性。