  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.
