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- W2795398890 abstract "HCI has become particularly interested in using machine learning (ML) to improve user experience (UX). However, some design researchers claim that there is a lack of design innovation in envisioning how ML might improve UX. We investigate this claim by analyzing 2,494 related HCI research publications. Our review confirmed a lack of research integrating UX and ML. To help span this gap, we mined our corpus to generate a topic landscape, mapping out 7 clusters of ML technical capabilities within HCI. Among them, we identified 3 under-explored clusters that design researchers can dig in and create sensitizing concepts for. To help operationalize these technical design materials, our analysis then identified value channels through which the technical capabilities can provide value for users: self, context, optimal, and utility-capability. The clusters and the value channels collectively mark starting places for envisioning new ways for ML technology to improve people's lives." @default.
- W2795398890 created "2018-04-13" @default.
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- W2795398890 date "2018-04-19" @default.
- W2795398890 modified "2023-10-10" @default.
- W2795398890 title "Mapping Machine Learning Advances from HCI Research to Reveal Starting Places for Design Innovation" @default.
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- W2795398890 doi "https://doi.org/10.1145/3173574.3173704" @default.
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