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Enabling Better Shopping Experiences Using Technology, Data, & Fashion Attributes
Presented by Rhonda Textor, Head of Data Science at True Fit Recent advances in technology such as computer vision, deep learning, and recommender systems are being used to enable new shopping experiences. Examples include visual search, recommending similar items, and recommending items that other...
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| Formato: | Electrónico Video |
| Idioma: | Inglés |
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Data Science Salon,
2019.
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| Edición: | 1st edition. |
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| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | Presented by Rhonda Textor, Head of Data Science at True Fit Recent advances in technology such as computer vision, deep learning, and recommender systems are being used to enable new shopping experiences. Examples include visual search, recommending similar items, and recommending items that other shoppers also viewed. However, technology alone without an understanding of shoppers, fashion, and retail falls short of solving shopping recommendation problems. We at True Fit believe that details matter, especially for modeling individual fashion preferences. Because of that, we have built the largest fashion and retail dataset called the Fashion Genome. In this talk, I will share some insights we have gained from the Fashion Genome that have influenced our approach to building fashion recommendation systems. I will also show how we leverage the fashion details of products, i.e., our Fashion Attributes, to combine fashion and technology to make great recommendations. Finally, I will share how we leverage our large dataset and cutting edge technology to enable personalized shopping experiences. |
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| Descripción Física: | 1 online resource (1 video file, approximately 29 min.) |