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To build a fashion recommendation system, we need to help users retrievefashionable items that are visually similar to a particular query, for reasonsranging from searching alternatives (i.e., substitutes), to generating stylishoutfits that are visually consistent, among other applications. In domains likeclothing and accessories, such considerations are particularly paramount as thevisual appearance of items is a critical feature that guides users' decisions.However, existing systems like Amazon and eBay still rely mainly on keywordsearch and recommending loosely consistent items (e.g. based on co-purchasingor browsing data), without an interface that makes use of visual information toserve the above needs. In this paper, we attempt to fill this gap by designingand implementing an image-based query system, called Fashionista, whichprovides a graphical interface to help users efficiently explore those itemsthat are not only visually similar to a given query, but which are alsofashionable, as determined by visually-aware recommendation approaches.Methodologically, Fashionista learns a low-dimensional visual space as well asthe evolution of fashion trends from large corpora of binary feedback data suchas purchase histories of Women's Clothing & Accessories from Amazon, which weuse for this demonstration.

Fashionista: A Fashion-aware Graphical System for Exploring Visually Similar Items