Abstract
In this demo paper, we present a prototype web application of a product search engine of a fashion e-shop. Although e-shop products consist of full-text description, relational attributes (e.g., price type, size, color, etc.) as well as visual information (product photo) traditional search engines in e-shops only provide full-text and relational attributes for product filtering.
In our retrieval model we incorporate also the visual information into the search by extracting visual-semantic features using deep convolutional neural networks. Furthermore, visual exploration of the product space using the visual-semantic features (multi-example queries) is used to dynamically discover latent visual attributes that could enhance the original relational schema by fuzzy attributes (e.g., a floral pattern in product).
In the demo, we show how these latent attributes could be used to recommend the user preferred products and even outfits (e.g., shoes, bag, jacket) that fit a certain visual style.