2010
DOI: 10.1007/s11257-010-9080-6
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User preferences can drive facial expressions: evaluating an embodied conversational agent in a recommender dialogue system

Abstract: Tailoring the linguistic content of automatically generated descriptions to the preferences of a target user has been well demonstrated to be an effective way to produce higher-quality output that may even have a greater impact on user behaviour. It is known that the non-verbal behaviour of an embodied agent can have a significant effect on users' responses to content presented by that agent. However, to date no-one has examined the contribution of non-verbal behaviour to the effectiveness of user tailoring in… Show more

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Cited by 9 publications
(9 citation statements)
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“…Especially, detecting knowledge gaps of students is essential to increase cognitive learning outcomes of students (Pérez et al, 2016). Moreover, detecting the emotional status and including relational behavior such as caring and empathy helps students to improve their affective learning outcomes (Foster & Oberlander, 2010). This is also the reason why chatbots should include small talk (Bickmore et al, 2013;Kerly et al, 2007).…”
Section: Context-awarenessmentioning
confidence: 99%
“…Especially, detecting knowledge gaps of students is essential to increase cognitive learning outcomes of students (Pérez et al, 2016). Moreover, detecting the emotional status and including relational behavior such as caring and empathy helps students to improve their affective learning outcomes (Foster & Oberlander, 2010). This is also the reason why chatbots should include small talk (Bickmore et al, 2013;Kerly et al, 2007).…”
Section: Context-awarenessmentioning
confidence: 99%
“…Various factors can impact the effectiveness of such ECAs. In [42], for example, the authors analyze the effects of non-verbal behavior (e.g., the facial expressions) on the effectiveness of an ECA in the context of a dialogue-based recommender system. Research on the specific effects of using different variants of an ECA in the context of recommender systems is, however, generally rare.…”
Section: Input and Output Modalitiesmentioning
confidence: 99%
“…Often, the CRS is implemented in the form of chatbot that is embedded within e-commerce solutions [32,156] or other types of web-portals [21]. In some cases, the CRS is also part of a multi-modal 2D or 3D user experience, like in [42] and [33]. A special case in this context is the use of a CRS on voice-based home assistants (smart speakers) [4,36].…”
Section: Application Environmentmentioning
confidence: 99%
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