Fashion Ecommerce is challenging because users have intents that could be very broad Eg: ''I want a dress to wear for my cousin's wedding''. Traditional single-shot retrieval mechanisms like search don't work very well for such intents. To solve this problem we developed ''Maya'', a conversational shopping assistant based on LLMs. Maya has been deployed in production and is available to select cohort of users at scale who come to our platform. It is arguably, one of the earliest production deployments of GenAI Fashion assistants at this scale. We describe the journey of building Maya and lessons post the production deployment.

Kernel methods yield state-of-the-art performance in certain applications such as image classification and object detection. However, large scale problems require machine learning techniques of at most linear complexity and these are usually limited to linear kernels. This unfortunately rules out gold-standard kernels such as the generalized RBF kernels (e.g. exponential-c 2 ). Recently, Maji and Berg [13] and Vedaldi and Zisserman [20] proposed explicit feature maps to approximate the additive kernels (intersection, c 2 , etc.) by linear ones, thus enabling the use of fast machine learning technique in a non-linear context. An analogous technique was proposed by Rahimi and Recht [14] for the translation invariant RBF kernels. In this paper, we complete the construction and combine the two techniques to obtain explicit feature maps for the generalized RBF kernels. Furthermore, we investigate a learning method using l 1 regularization to encourage sparsity in the final vector representation, and thus reduce its dimension. We evaluate this technique on the VOC 2007 detection challenge, showing when it can improve on fast additive kernels, and the trade-offs in complexity and accuracy.