Topic Recommendation to Expand Knowledge and Interest in Question-and-Answer Agents

By providing a high degree of freedom to explore information, QA (question and answer) agents in museums are expected to help visitors gain knowledge on a range of exhibits. Since information exploration with a QA agent often involves a series of interactions, proper guidance is required to support...

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Autores principales:	Albert Deok-Young Yang, Yeo-Gyeong Noh, Jin-Hyuk Hong
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	context-aware services education technology human–computer interaction learning management systems Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/1f39fd8463944f0e9613384e9d86f605
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spelling	oai:doaj.org-article:1f39fd8463944f0e9613384e9d86f6052021-11-25T16:33:05ZTopic Recommendation to Expand Knowledge and Interest in Question-and-Answer Agents10.3390/app1122106002076-3417https://doaj.org/article/1f39fd8463944f0e9613384e9d86f6052021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10600https://doaj.org/toc/2076-3417By providing a high degree of freedom to explore information, QA (question and answer) agents in museums are expected to help visitors gain knowledge on a range of exhibits. Since information exploration with a QA agent often involves a series of interactions, proper guidance is required to support users as they find out what they want to know and broaden their knowledge. In this paper, we validate topic recommendation strategies of system-initiative QA agents that suggest multiple topics in different ways to influence users’ information exploration, and to help users proceed to deeper levels in topics on the same subject, to offer them topics on various subjects, or to provide them with selections at random. To examine how different recommendations influence users’ experience, we have conducted a user study with 50 participants which has shown that providing recommendations on various subjects expands their interest on subjects, supports longer conversations, and increases willingness to use QA agents in the future.Albert Deok-Young YangYeo-Gyeong NohJin-Hyuk HongMDPI AGarticlecontext-aware serviceseducation technologyhuman–computer interactionlearning management systemsTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10600, p 10600 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	context-aware services education technology human–computer interaction learning management systems Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	context-aware services education technology human–computer interaction learning management systems Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Albert Deok-Young Yang Yeo-Gyeong Noh Jin-Hyuk Hong Topic Recommendation to Expand Knowledge and Interest in Question-and-Answer Agents
description	By providing a high degree of freedom to explore information, QA (question and answer) agents in museums are expected to help visitors gain knowledge on a range of exhibits. Since information exploration with a QA agent often involves a series of interactions, proper guidance is required to support users as they find out what they want to know and broaden their knowledge. In this paper, we validate topic recommendation strategies of system-initiative QA agents that suggest multiple topics in different ways to influence users’ information exploration, and to help users proceed to deeper levels in topics on the same subject, to offer them topics on various subjects, or to provide them with selections at random. To examine how different recommendations influence users’ experience, we have conducted a user study with 50 participants which has shown that providing recommendations on various subjects expands their interest on subjects, supports longer conversations, and increases willingness to use QA agents in the future.
format	article
author	Albert Deok-Young Yang Yeo-Gyeong Noh Jin-Hyuk Hong
author_facet	Albert Deok-Young Yang Yeo-Gyeong Noh Jin-Hyuk Hong
author_sort	Albert Deok-Young Yang
title	Topic Recommendation to Expand Knowledge and Interest in Question-and-Answer Agents
title_short	Topic Recommendation to Expand Knowledge and Interest in Question-and-Answer Agents
title_full	Topic Recommendation to Expand Knowledge and Interest in Question-and-Answer Agents
title_fullStr	Topic Recommendation to Expand Knowledge and Interest in Question-and-Answer Agents
title_full_unstemmed	Topic Recommendation to Expand Knowledge and Interest in Question-and-Answer Agents
title_sort	topic recommendation to expand knowledge and interest in question-and-answer agents
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1f39fd8463944f0e9613384e9d86f605
work_keys_str_mv	AT albertdeokyoungyang topicrecommendationtoexpandknowledgeandinterestinquestionandansweragents AT yeogyeongnoh topicrecommendationtoexpandknowledgeandinterestinquestionandansweragents AT jinhyukhong topicrecommendationtoexpandknowledgeandinterestinquestionandansweragents
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Topic Recommendation to Expand Knowledge and Interest in Question-and-Answer Agents

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