Abstract
Conversational Voice User Interfaces (VUIs) help us in performing tasks in a wide range of domains these days. While there have been several efforts around designing dialogue systems and conversation flows, little information is available about technical concepts to extract critical information for addressing the users’ needs. For conversational VUIs to function appropriately as a decision aid, artificial intelligence (AI) that recognizes and supports diverse user decision strategies is a critical need. Following the design principle proposed by Kwon et al. [1] regarding the conversational flow between the user and conversational VUI, we developed an AI-based mobile-decision-aid (MODA) that predictively models and addresses users’ decision strategies to facilitate users’ in-store shopping decision process. In this paper, technical details about how MODA processes users’ natural language queries and generate the most appropriate and intelligent recommendations have been discussed. This developmental approach provides broad implications to conversational VUIs for diverse complex decision-making contexts and decision-makers with a critical need for decision assistants.
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References
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Acknowledgements
This material is based in part upon work supported by the National Science Foundation under Grant Numbers IIS-1527182 and IIS-1527302; the Alabama Agricultural Experiment Station; and the Hatch program of the National Institute of Food and Agriculture, U.S. Department of Agriculture. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies acknowledged above.
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Alikhademi, K. et al. (2019). AI-Based Technical Approach for Designing Mobile Decision Aids. In: Stephanidis, C. (eds) HCI International 2019 - Posters. HCII 2019. Communications in Computer and Information Science, vol 1033. Springer, Cham. https://doi.org/10.1007/978-3-030-23528-4_23
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DOI: https://doi.org/10.1007/978-3-030-23528-4_23
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