Selene Xu
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Staff Data Scientist
Walmart Global Tech |
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Selene is a Staff Data Scientist at Walmart Global Tech in Sunnyvale, California, and has experience in statistical modeling & machine learning. She obtained my PhD in Statistics at UC San Diego. Before joining Walmart, Selene worked at Ancestry as a Data Scientist. As a part of the Walmart personalization team, she's building machine learning models to facilitate Walmart's item understanding and customer understanding. Selene designs scalable recommender systems leveraging these learnings and provides relevant and useful suggestions to customers throughout their shopping journey.
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Workshop: Recommender Systems
August 25, 2021; 10:15-11:00am, PST
August 25, 2021; 10:15-11:00am, PST
Recommender systems are playing a major role in e-commerce industry. They are keeping users engaged by recommending relevant content and have a significant role in driving digital revenue.
Following tremendous gains in computer vision and natural language processing with deep neural networks in the past decade, the recent years have seen a shift from traditional recommender systems to deep neural network architectures in research and industry.
In this workshop, we focus on temporal domain from perspective of both traditional recommender systems and deep neural networks. We first start with the classic latent factor model. We introduce temporal dynamics in the latent factor model and show how this improves performance. We then move into sequential modelling using deep neural networks by presenting state-of-the-art in the field and discuss the advantages and disadvantages.
Following tremendous gains in computer vision and natural language processing with deep neural networks in the past decade, the recent years have seen a shift from traditional recommender systems to deep neural network architectures in research and industry.
In this workshop, we focus on temporal domain from perspective of both traditional recommender systems and deep neural networks. We first start with the classic latent factor model. We introduce temporal dynamics in the latent factor model and show how this improves performance. We then move into sequential modelling using deep neural networks by presenting state-of-the-art in the field and discuss the advantages and disadvantages.
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