In our first exploration in this space, we develop a model that can learn latent fashion concepts from data. Using polylingual topic models (PLTMs), we learn latent fashion concepts joinly in two languages: a style language describing outfits and an element language labeling clothing items. This model allows us to translate between the two languages, exposing the elements of fashion style.
The second project investigates consumer pain points and opportunities for tech interventions. To do so, we conduct two need-finding studies that explore the gold-standard of personalized shopping: interacting with a personal stylist. These results capture both interviews with working personal stylists as well as conversational logs from user interactions with a personal stylist chatbot -- PSBot.
Kristen Vaccaro, Tanvi Agarwalla, Sunaya Shivakumar and Ranjitha Kumar. Designing the Future of Personal Fashion. CHI 2018. pdf
Ranjitha Kumar and Kristen Vaccaro. An Experimentation Engine for Data-Driven Fashion Systems. AAAI 2017 Spring Symposium. pdf
Kristen Vaccaro, Sunaya Shivakumar, Ziqiao Ding, Karrie Karahalios and Ranjitha Kumar. The Elements of Fashion Style. UIST 2016. pdf