Session:「Design and Design Research 2」

Analogy Mining for Specific Design Needs

論文URL: http://dl.acm.org/citation.cfm?doid=3173574.3173695

論文アブストラクト: Finding analogical inspirations in distant domains is a powerful way of solving problems. However, as the number of inspirations that could be matched and the dimensions on which that matching could occur grow, it becomes challenging for designers to find inspirations relevant to their needs. Furthermore, designers are often interested in exploring specific aspects of a product-- for example, one designer might be interested in improving the brewing capability of an outdoor coffee maker, while another might wish to optimize for portability. In this paper we introduce a novel system for targeting analogical search for specific needs. Specifically, we contribute an analogical search engine for expressing and abstracting specific design needs that returns more distant yet relevant inspirations than alternate approaches.

日本語のまとめ:

とあるひらめきに関連する事例を異なる分野から見つけることは問題解決の手段として強力である。抽象的な文章から類似した製品を検索するシステムを提案。既存の類似システムと比較実験し、提案システムが今後に貢献できると主張。

Mapping Machine Learning Advances from HCI Research to Reveal Starting Places for Design Innovation

論文URL: http://dl.acm.org/citation.cfm?doid=3173574.3173704

論文アブストラクト: 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.

日本語のまとめ:

機械学習を用いたユーザーエクスペリエンス向上に設計革新が欠けているという意見がある。HCI研究での2494個の発表を分析し、本分野での設計機会の体系化を目指した。結果、ユーザ評価の難しさから議論を始めることを推奨した。