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In this talk, you will leam Soraya and Roberto's journey in the design and development of recommender systems for their platform, beginning with a discussion of the technical and business challenges they faced when starting to build their recommendation engine. Next, you will explore the main characteristics of the fashion domain and how they approached the importance of incorporating domain knowledge within their recommendation framework.
You will then learn various use cases Soraya and Roberto have been working on, such as product recommendations, related products, out of stock recommendations, category recommendations and visual browsing. This is followed by an illustration of how various important other functions and elements contribute to the success of a recommender system and what specific challenges they faced in putting their algorithms into a production environment.
The talk will conlude with an outlining of their data science roadmap, which includes context-aware recommendations, session-based recommendations and tensor decomposition techniques.
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Fashion Recommendations at ASOS: Challenges, Approaches and Learnings
Soraya Hausl is a Senior Data Scientist at ASOS where she leads the Recommendations Team. She is passionate about building data products that improve customer experience. Prior to ASOS, Soraya obtained a MSc degree in Machine Learning from the University College London (UCL) and has worked in strategy consulting.
Roberto Pagliari is a Data Scientist at ASOS, where he works in recommender systems and leads the R\&D activities within the Data Science Team. He was previously with Vencore Labs and Qualcomm Inc. and holds a Ph.D. in Electrical Engineering from Cornell University.