Prior to commencing production, one of the crucial business choices in the fashion industry is the selection of products to feature in an upcoming collection. This process involves supply chain decisions at all levels and aims to determine the optimal product lines, which are compilations of products to be included in the assortment. As noted in [1], successful assortment planning requires a balance among several factors, including the number of categories (breadth), the number of items or stock-keeping units (SKUs) in each category (depth), and the amount of inventory allocated to each SKU. The ultimate goal of the assortment planning process is to maximize sales or revenue by offering appropriate merchandise that enables consumers to find and purchase what they want. In this work, we are focused on the task of selecting the subset of products for a retail store from a large set of options available for assortment planning. In terms of the broader literature on fashion assortment curation, our work is closely related to the areas of fashion sales and demand forecasting [29, 30], fashion popularity prediction [18, 31], and multi-modal learning for forecasting $[33,34]$. We provide a more detailed overview of the existing research in these fields.Rewrite the above content in a new way: