Improving Size Recommendations in High-End Fashion Marketplaces

Accurate and personalized size recommendations are essential in the ever-changing and dynamic realm of high-end fashion marketplaces. These recommendations not only satisfy customer expectations but also contribute significantly to customer retention, a crucial metric for the success of any fashion retailer. To address this challenge, a novel sequence classification approach is proposed, incorporating both implicit (Add2Bag) and explicit (ReturnReason) user signals.

The approach consists of two distinct models. The first model utilizes Long Short-Term Memory (LSTM) networks to encode the user signals, capturing the temporal aspect of user behavior. This allows the model to understand patterns in the data and make better size recommendations based on the sequence of user interactions. The second model incorporates an Attention mechanism, which enables the model to weigh the importance of different user signals when making size recommendations.

The results demonstrate that the proposed approach outperforms the SFNet model, achieving a significant improvement in accuracy by 45.7%. By leveraging Add2Bag interactions in addition to Orders, the user coverage is increased by 24.5%. This means that more users can benefit from accurate size recommendations, leading to increased customer satisfaction and potentially higher conversion rates.

In addition to accuracy and user coverage, the usability of the models in real-time recommendation scenarios is also evaluated. The experiments measure the latency performance of the models, ensuring that they can provide size recommendations quickly enough to be useful during browsing and shopping sessions. Fast and responsive recommendations are important for enhancing the user experience and driving customer engagement.

Looking ahead, further developments and improvements could be made to enhance the proposed approach. For instance, exploring alternative deep learning architectures or incorporating additional user signals could potentially improve accuracy even further. Additionally, considering contextual information such as weather or occasion could provide more personalized and relevant recommendations. Overall, this research presents a promising step towards revolutionizing the size recommendation process in high-end fashion marketplaces and ultimately improving customer satisfaction and retention.

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