kddfashion2017.mybluemix.netKDD2017 | Machine learning meets fashion

KDD2017 Workshop Machine learning meets fashion Data, algorithms and analytics for the fashion industry 14 August 2017, Halifax, Nova Scotia - Canada Background Topics of interest Workshop Schedule Invited Speakers Invited Talk Abstracts -- Accepted Papers/Posters Organizers PC Members Submission Back to top Background Fashion is a multi-billion-dollar industry with social and economic implications worldwide. The fashion industry has traditionally placed high value on human creativity and has been slower to realize the potential of data analytics. With the advent of modern cognitive computing technologies (data mining and knowledge discovery, machine learning, deep learning, computer vision, natural language understanding etc.) and vast amounts of (structured and unstructured) fashion data the impact on fashion industry could be transformational. Already fashion e-commerce portals are using data to be branded as not just an online warehouse, but also as a fashion destination. Luxury fashion houses are planning to recreate physical in-store experience for their virtual channels, and a slew of technology startups are providing trending, forecasting, and styling services to fashion industry. The second international workshop on fashion and KDD will be hosted at KDD 2017 in Halifax, Nova Scotia - Canada on 14th August, 2017. The goal of this workshop is to gather people from academia, industry, and startups working at the intersection of fashion and data mining and knowledge discovery to further the technology and its adoption. The first international workshop on fashion and KDD was organized at KDD 2016 and was a big success. workshop schedule -- submit a paper -- KDD 2017 Workshop Schedule KDD2016 workshop Topics of Interest This is a new emerging area for the KDD community and we hope this workshop will bring together all the researchers, practitioners, and interested audiences to explore the open problems, applications, and future directions in this field. We believe that the fashion industry introduces a number of interesting data analytics problems that are either not studied or scarcely studied in the past and can attract great interest in the general KDD community given their practical implications. The first international workshop on fashion and KDD was organized at KDD 2016 and was a big success. Suggested topics include (but not limited to): Detect and forecast fashion trends and cycles Big data for fast fashion (the like of Zara, H&M, and Primark) Analyzing fashion blogs, articles, and images Visual search for fashion e-commerce Fashion image understanding and auto-tagging of apparel Novel search mechanisms for large fashion catalogs Virtual personal fashion assistants Recommendation engines and cognitive stylists for fashion Balancing art and science in fashion recommendation algorithms Personal styling with humans and machines: recommendations with humans in the loop Assembling outfit recommendations: interactions and serendipity Algorithmic clothing: design by data Predicting fashionability scores Social networking for fashion Fashion retail analytics Interactive textiles Digital wardrobe Mining style rules Assessing fashion personality (from social media platforms) Virtual trial rooms Plagiarism detection in fashion Fashion and wearable computing Technology in fashion weeks We also invite submissions in other retail domains where design, trends, styling, recommendations are important (for example, jewelry, furniture etc.). Workshop Schedule 14th August, 2017 afternoon (1pm - 5pm) 1:00 - 1:30 pm - Welcome and invited talk by Kavita Bala on Fashion and Style Discovery: object and material recognition from online photo collections 1:30 - 2:30 pm - Oral Paper Presentations 1:30 - 1:50 pm - Size Recommendation System for Fashion E-commerce 1:50 - 2:10 pm - Learning Fashion Traits with Label Uncertainty 2:10 - 2:30 pm - Sales Potential : Modeling Sellability of Fashion Product 2:30 - 3:00 pm - Invited talk by Madhu Kurup on Making fashion recommendations in cold start situations 3:00 - 3:30 pm - Invited Talk 2:30 - 3:00 pm - Invited talk by Brad Klingenberg 3:00 - 3:30 pm - Invited talk by Julian McAuley on Recommendation and Opinion Mining with Visual Signals -- 3:00 - 3:30 pm - Coffee break & Poster Session 3:30 - 4:30 pm - Oral Paper Presentations 3:30 - 3:50 pm - Cross-modal Search for Fashion Attributes 3:50 - 4:10 pm - Algorithmic clothing: hybrid recommendation, from street-style-to-shop 4:10 - 4:30 pm - Deciphering Fashion Sensibility Using Community Detection 4:30 - 5:00 - Panel discussion led by Menaka Sampath followed by an optional Open House Invited Speakers Kavita Bala Professor, Cornell University Brad Klingenberg is the Director of Data Science at Stitch Fix in San Francisco. His team uses data and algorithms to improve the selection of merchandise sent to clients. Prior to joining Stitch Fix Brad worked with data and predictive analytics at financial and technology companies. He studied applied mathematics at the University of Colorado at Boulder and earned his PhD in Statistics at Stanford University in 2012. -- Madhu Kurup Director of Amazon Personalization Elena Eberhard is a Public Relations and Special Events Manager in the Academy of Art University School of Fashion. She started as a Public Relations Director for Parfionova, a leading fashion house in Russia. After moving to San Francisco from Paris in 2013, Elena managed international relations and expansion on US and Russian market for German-based fashion industry tradeshow Premium Berlin and consulted fashion startups on international business development, as well as gave talks on Fashion Wearable Tech at Silicon Valley conferences. Throughout her international career of 18 years she attended numerous fashion weeks and tradeshows all over Europe and USA, as well as organized fashion shows and events, and contributed as a free-lance journalist for fashion and culture medias in Russia and France. -- TBD Dr. McAuley has been an Assistant Professor in the Computer Science Department at the University of California, San Diego since 2014. Previously he was a postdoctoral scholar at Stanford University after receiving his PhD from the Australian National University in 2011. His research is concerned with developing predictive models of human behavior using large volumes of online activity data. -- Invited Talks Challenges of quantifying fashion data: creativity, art and emotions Elena Eberhard To be decided Brad Klingenberg Recommendation and Opinion Mining with Visual Signals Julian McAuley Building personalized systems for fashion recommendation presents several challenges due to the complicated semantics of people's preferences and styles. One challenge is simply the need to deal with sparse, long-tailed datasets, where new content is constantly introduced and recommendation is inherently a cold-start problem. Another challenge is the need to model visual signals, where the semantics of what makes items "attractive" are incredibly subtle. Finally, there is the need to model temporal dynamics that account for how fashion continually (and rapidly) evolves. In this talk we'll see how traditional recommendation approaches can be extended to explicitly account for the visual appearance of the items being recommended, in order to overcome these challenges and make visually- and stylistically-aware recommendations. -- Accepted Papers/Posters Accepted Papers for Oral Presentation G. Mohammed Abdulla, Sumit Borar. Size Recommendation System for Fashion Ecommerce Assaf Neuberger, Eli Alshan, Gal Levi, Sharon Alpert and Eduard Oksm. Learning Fashion Traits with Label Uncertainty Vikram Garg, Rajdeep H Banerjee, Anoop Kolar Rajagopal, Sreenivas Thiruvambalam and Deepak Warrier. Sales Potential : Modeling Sellability of Fashion Product Katrien Laenen, Susana Zoghbi and Marie-Francine Moens. Cross-modal Search for Fashion Attributes Yu Qian, P Giaccone, M Sasdelli, E Vazquez ...