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FashionBrain SIGNED

Understanding Europe’s Fashion Data Universe

Total Cost €


EC-Contrib. €






 FashionBrain project word cloud

Explore the words cloud of the FashionBrain project. It provides you a very rough idea of what is the project "FashionBrain" about.

retail    shipping    detection    surprisingly    itself    creation    tangible    customers    logical    customer    concise    machine    fashionbrain    solid    understand    gains    area    algorithms    supplier    events    sufficient    improvement    data    demonstrators    fashion    tastes    provides    handling    prediction    perspective    obtain    influencers    position    stocking    outcome    consequence    cultures    software    retailer    cycle    competitors    shopping    consolidate    efficiency    upcoming    logistics    strengthen    purchases    demand    technologies    line    positions    habits    learning    personalities    languages    life    predicting    mining    aspirations    chain    thanks    demands    business    complete    items    moverover    image    catalog    primary    diverse    reaction    outcomes    proactive    trigger    world    returns    experiences    sectorial    keep    appears    minor    database    economy    retailers    crowdsourcing    billion    industry    extend    record    lifestyle    stage    interviews    integration    extremely    retrieval    connect    company    excellent    exercise    imposes   

Project "FashionBrain" data sheet

The following table provides information about the project.


Organization address
postcode: S10 2TN

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Project website
 Total cost 2˙794˙448 €
 EC max contribution 1˙699˙323 € (61%)
 Programme 1. H2020-EU.2.1.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT))
 Code Call H2020-ICT-2016-1
 Funding Scheme IA
 Starting year 2017
 Duration (year-month-day) from 2017-01-01   to  2019-12-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    THE UNIVERSITY OF SHEFFIELD UK (SHEFFIELD) coordinator 419˙381.00
3    MONETDB SOLUTIONS BV NL (AMSTERDAM) participant 355˙250.00
4    ZALANDO SE DE (BERLIN) participant 333˙025.00
5    FASHWELL AG CH (ZURICH) participant 0.00


 Project objective

The primary goal of each retailer is to “understand your customers”. Our interviews with retailers show a primary demand from the retail industry for predicting a customer's next demand. Surprisingly , even a complete record of past purchases (and returns) is not sufficient to understand how items in a company's catalog do or do not connect with the customer's general tastes, lifestyle and aspirations. Moverover, from a business perspective, any efficiency gains in the logistics of supplier management, shipping and handling are rather minor, compared to the gains one could obtain from a better understanding of the customers’ personalities and habits. Given that the customer demands trigger proactive stocking and fashion production, this appears as a logical consequence. In this project, we want to consolidate and extend existing European technologies in the area of database management, data mining, machine learning, image processing, information retrieval, and crowdsourcing to strengthen the positions of European fashion retailers among their world-wide competitors. Our choice for the fashion sector is a concise one: i) as a multi-billion euro industry, the fashion sector is extremely important for the European economy; ii) Europe already has a solid position in the world fashion stage, however, to maintain its position and keep up with the competitors, European fashion industry needs the help of advanced technology; and iii) European fashion industry provides an excellent exercise for new technologies, because it is a multi-sectorial by itself (i.e., imposes challenging data integration issues), it has a short life-cycle (i.e., requires timely reaction to the current events) and it involves diverse languages and cultures. The main outcome of the FashionBrain project is the improvement of the fashion industry value chain obtained thanks to the creation of novel on-line shopping experiences, the detection of influencers, and the prediction of upcoming fashion trends. Tangible outcomes will include software, demonstrators, and novel algorithms for a data-driven fashion industry.


List of deliverables.
Project factsheet Documents, reports 2020-04-09 10:55:20
Named Entity Recognition and Linking methods Other 2020-04-09 10:55:20
Project Web site Websites, patent fillings, videos etc. 2020-04-09 10:55:20
Report on text joins Documents, reports 2020-04-09 10:55:20
Early Demo on textual image search Demonstrators, pilots, prototypes 2020-04-09 10:55:20
The classification algorithm and its evaluation on fashion time series Other 2020-04-09 10:55:20
Demo on Fashion Trend Prediction Demonstrators, pilots, prototypes 2020-04-09 10:55:20
Communication plan Documents, reports 2020-04-09 10:55:20
Early Demo on Fashion Trend Prediction Demonstrators, pilots, prototypes 2020-04-09 10:55:20
Surveys design and crowdsourcing tasks Documents, reports 2020-04-09 10:55:20
Relation Extraction with Stacked Deep Learning Documents, reports 2020-04-09 10:55:20
Product Taxonomy Linking Demonstrators, pilots, prototypes 2020-04-09 10:55:20
Demo on text joins Demonstrators, pilots, prototypes 2020-04-09 10:55:19
A set of crowdsourcing interfaces Other 2020-04-09 10:55:19
Survey document of existing datasets and data integration solutions (M6) Documents, reports 2020-04-09 10:55:20
Scalable Crowdsourced Social Media Annotation Demonstrators, pilots, prototypes 2020-04-09 10:55:20
Software Requirements: SSM library for time series modelling and trend prediction Documents, reports 2020-04-09 10:55:20
Data integration solution Other 2020-04-09 10:55:20
A set of aggregation algorithms and their experimental evaluation Other 2020-04-09 10:55:20
Showcase specification Documents, reports 2020-04-09 10:55:20

Take a look to the deliverables list in detail:  detailed list of FashionBrain deliverables.


year authors and title journal last update
List of publications.
2020 Ines Arous, Jie Yang, Mourad Khayati and Philippe Cudré-Mauroux
OpenCrowd: Leveraging Open-Ended Answers Aggregation for Finding Social Influencers
published pages: , ISSN: , DOI:
2019 Rehab Qarout, Alessandro Checco, Gianluca Demartini and Kalina Bontcheva
Platform-related Factors in Repeatability and Reproducibility of Crowdsourcing Tasks
published pages: , ISSN: , DOI:
2019 Lei Han, Kevin Roitero, Ujwal Gadiraju, Cristina Sarasua, Alessandro Checco, Eddy Maddalena, Gianluca Demartini
The Impact of Task Abandonment in Crowdsourcing
published pages: 1-1, ISSN: 1041-4347, DOI: 10.1109/tkde.2019.2948168
IEEE Transactions on Knowledge and Data Engineering 2020-04-09
2018 Rehab K. Qarout, Alessandro Checco, Kalina Bontcheva
Investigating Stability and Reliability of Crowdsourcing Output
published pages: , ISSN: , DOI:
CrowdBias 2018 2020-04-09
2019 Mourad Khayati, Philippe Cudré-Mauroux, Michael H. Böhlen
Scalable recovery of missing blocks in time series with high and low cross-correlations
published pages: , ISSN: 0219-1377, DOI: 10.1007/s10115-019-01421-7
Knowledge and Information Systems 2020-04-09
2019 Djellel Difallah, Alessandro Checco, Gianluca Demartini, Philippe Cudré-Mauroux
Deadline-Aware Fair Scheduling for Multi-Tenant Crowd-Powered Systems
published pages: 1-29, ISSN: 2469-7818, DOI: 10.1145/3301003
ACM Transactions on Social Computing 2/1 2020-04-09
2020 Alessandro Checco, Jo Bates, Gianluca Demartini
Adversarial Attacks on Crowdsourcing Quality Control
published pages: , ISSN: 1076-9757, DOI:
Journal of Artificial Intelligence Research 2020-04-09
2019 Alan Akbik, Tanja Bergmann, Duncan Blythe, Kashif Rasul, Stefan Schweter and Roland Vollgraf
FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP
published pages: , ISSN: , DOI:
NAACL-HLT 2019 2020-04-09
2019 Alan Akbik, Tanja Bergmann and Roland Vollgraf
Multilingual Sequence Labeling With One Model
published pages: , ISSN: , DOI:
NLDL 2019 2020-04-09
2019 Alan Akbik, Tanja Bergmann and Roland Vollgraf
Pooled Contextualized Embeddings for Named Entity Recognition
published pages: , ISSN: , DOI:
NAACL-HLT 2019 2020-04-09
2018 Ying Zhang, Richard Koopmanschap, Martin L. Kersten
Love at First Sight: MonetDB/TensorFlow
published pages: , ISSN: , DOI:
34th IEEE International Conference on Data Engineering 2020-04-09
2017 Ujwal Gadiraju, Alessandro Checco, Neha Gupta, Gianluca Demartini
Modus Operandi of Crowd Workers
published pages: 1-29, ISSN: 2474-9567, DOI: 10.1145/3130914
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1/3 2020-04-09
2018 Alan Akbik, Duncan Blythe and Roland Vollgraf
Contextual String Embeddings for Sequence Labeling
published pages: , ISSN: , DOI:
27th International Conference on Computational Linguistics, COLING 2018 2020-04-09
2018 Torsten Kilias, Alexander Löpser, Felix A. Gers, Richard Koopmanschap, Ying Zhang, Martin Kersten, Mark Raasveldt, Pedro Holanda, Hannes Mühleisen and Stefan Manegold
In-Database Machine Learning with MonetDB/TensorFlow
published pages: , ISSN: , DOI:
2017 Alan Akbik, Roland Vollgraf
The Projector: An Interactive Annotation Projection Visualization Tool
published pages: 43-48, ISSN: , DOI: 10.18653/v1/D17-2008
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations 2020-04-09
2018 Kilias, Torsten; Löser, Alexander; Gers, Felix A.; Koopmanschap, Richard; Zhang, Ying; Kersten, Martin
IDEL: In-Database Entity Linking with Neural Embeddings
published pages: 12, ISSN: , DOI:
1 2020-04-09
2017 Alessandro Checco, Kevin Roitero, Eddy Maddalena, Stefano Mizzaro and Gianluca Demartini
Let\'s Agree to Disagree: Fixing Agreement Measures for Crowdsourcing
published pages: , ISSN: , DOI:
2018 Leonidas Lefakis, Alan Akbik, Roland Vollgraf
FEIDEGGER: A Multi-modal Corpus of Fashion Images and Descriptionsin German
published pages: , ISSN: , DOI:
2018 Alan Akbik and Roland Vollgraf
ZAP: An Open-Source Multilingual Annotation Projection Framework
published pages: , ISSN: , DOI:
11th Language Resources and Evaluation Conference, LREC 2018 2020-04-09
2018 Cristina Sarasua, Alessandro Checco, Gianluca Demartini, Djellel Difallah, Michael Feldman, and Lydia Pintscher
The Evolution of Power and Standard Wikidata Editors: Comparing Editing Behavior over Time to Predict Lifespan and Volume of Edits
published pages: , ISSN: 0925-9724, DOI:
Computer Supported Cooperative Work (CSCW) Special Issue on Crowd Dynamics: Conflicts, Contradictions, and Cooperation Issues in Crowdsourcing 2020-04-09
2017 Rudolf Schneider, Tom Oberhauser, Tobias Klatt, Felix A. Gers, and Alexander Löser
RelVis: Benchmarking OpenIE Systems
published pages: , ISSN: , DOI:
International Semantic Web Conference (Posters, Demos & Industry Tracks) 2017 2020-04-09
2018 Alessandro Checco, Jo Bates and Gianluca Demartini
All That Glitters is Gold - An Attack Scheme on Gold Questions in Crowdsourcing
published pages: , ISSN: , DOI:
The sixth AAAI Conference on Human Computation and Crowdsourcing 2020-04-09

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