An experimentation engine for data-driven fashion systems

Ranjitha Kumar, Kristen Vaccaro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Data-driven fashion systems of the future will revolutionize the way consumers shop for clothing and choose outfits: imagine an automated personal stylist that ships clothes straight to your door based on their compatibility with your existing wardrobe, the upcoming events on your calendar, and style trends learned from the web. To build such systems, we must identify the fashion activities that are the largest consumer pain points, the interventions necessary to alleviate those pains, and the computational models that enable those interventions. To guide the design of these next-generation tools, we propose an experimentation engine for fashion interfaces: leveraging social media platforms to run multivariate design tests with thousands to millions of users. Social platforms are already home to dedicated communities of fashion enthusiasts, and expose programmable agents - chatbots - that can be used to rapidly prototype data-driven design interfaces. Measuring the number of followers and user engagement amongst these prototypes can inform the design of future standalone fashion systems. At this workshop, we will sketch the design space of fashion experiments, and present preliminary results from deploying our "fashion bots."

Original languageEnglish (US)
Title of host publicationSS-17-01
Subtitle of host publicationArtificial Intelligene for the Social Good; SS-17-02: Computational Construction Grammar and Natural Language Understanding; SS-17-03: Computational Context: Why It's Important, What It Means, and Can It Be Computed?; SS-17-04: Designing the User Experience of Machine Learning Systems; SS-17-05: Interactive Multisensory Object Perception for Embodied Agents; SS-17-06: Learning from Observation of Humans; SS-17-07: Science of Intelligence: Computational Principles of Natural and Artificial Intelligence; SS-17-08: Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing
PublisherAI Access Foundation
Pages389-394
Number of pages6
ISBN (Electronic)9781577357797
StatePublished - Jan 1 2017
Event2017 AAAI Spring Symposium - Stanford, United States
Duration: Mar 27 2017Mar 29 2017

Publication series

NameAAAI Spring Symposium - Technical Report
VolumeSS-17-01 - SS-17-08

Other

Other2017 AAAI Spring Symposium
CountryUnited States
CityStanford
Period3/27/173/29/17

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ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Kumar, R., & Vaccaro, K. (2017). An experimentation engine for data-driven fashion systems. In SS-17-01: Artificial Intelligene for the Social Good; SS-17-02: Computational Construction Grammar and Natural Language Understanding; SS-17-03: Computational Context: Why It's Important, What It Means, and Can It Be Computed?; SS-17-04: Designing the User Experience of Machine Learning Systems; SS-17-05: Interactive Multisensory Object Perception for Embodied Agents; SS-17-06: Learning from Observation of Humans; SS-17-07: Science of Intelligence: Computational Principles of Natural and Artificial Intelligence; SS-17-08: Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing (pp. 389-394). (AAAI Spring Symposium - Technical Report; Vol. SS-17-01 - SS-17-08). AI Access Foundation.