Product exploration based on latent visual attributes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10366342" target="_blank" >RIV/00216208:11320/17:10366342 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1145/3132847.3133175" target="_blank" >http://dx.doi.org/10.1145/3132847.3133175</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3132847.3133175" target="_blank" >10.1145/3132847.3133175</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Product exploration based on latent visual attributes

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Product exploration based on latent visual attributes

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA17-22224S" target="_blank" >GA17-22224S: Analytika uživatelských preferencí v modelech multimediální explorace</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

ISBN

978-1-4503-4918-5

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

4

Strana od-do

2531-2534

Název nakladatele

ACM

Místo vydání

New York

Místo konání akce

Singapore

Datum konání akce

6. 11. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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