The Academic Fringe Festival - Vanessa Murdock: Quality

16 May 2022 16:00 - Location: Oline | Add to my calendar

by Vanessa Murdock | Amazon

Abstract

Quality is a vague and undefined term, and yet it is an important consideration when we decide whether to buy something. In eCommerce, since people cannot physically touch or try an item before buying it, they must assess the quality with information available online. In this talk we discuss the notion of quality and ways it has historically been defined and measured. We present recent research in Alexa Shopping that addresses unique considerations in improving people’s ability to assess information online, and we discuss how people judge the quality of an item they are considering purchasing.

Speaker Biography

Vanessa Murdock leads a research group in Alexa Shopping at Amazon, whose focus is recommender systems, Search, AR/VR and HCI. Her team provides the machine learning that backs Amazon’s Choice. Previously, she worked at Microsoft as a Principal Scientist, working on location inference and notifications at Bing and Cortana. Prior to Microsoft, Murdock led the Geographic Context and Experience Group at Yahoo! Research in Barcelona, doing research on topics related to geographic information retrieval and user-generated content. She has been awarded 19 patents, and has more than 20 patent applications pending, resulting in a Master Inventor Award from Yahoo! (2012). She received the OAA Award for Outstanding Achievement by a Young Alum from the University of Massachusetts in 2014. Murdock received a Ph.D. in Computer Science from the University of Massachusetts Amherst in 2006, advised by Bruce Croft.

Homepage: https://www.amazon.science/vanessa-murdock-is-a-classical-pianist-turned-amazon-applied-scientist .

More information

In this second edition on the topic of "Responsible Use of Data", we take a multi-disciplinary view and explore further lessons learned from success stories and examples in which the irresponsible use of data can create and foster inequality and inequity, perpetuate bias and prejudice, or produce unlawful or unethical outcomes. Our aim is to discuss and draw certain guidelines to make the use of data a responsible practice.

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