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User Modeling and User-Adapted Interaction最新文献

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Preface to the special issue on personalization and adaptation in human–robot interactive communication 人与机器人互动交流中的个性化和适应性特刊前言
IF 3.6 3区 计算机科学 Q1 Social Sciences Pub Date : 2023-04-01 DOI: 10.1007/s11257-023-09365-y
Silvia Rossi, M. Staffa, M. D. Graaf, Cristina Gena
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引用次数: 1
Deploying a robotic positive psychology coach to improve college students' psychological well-being. 部署机器人积极心理学教练提高大学生心理健康水平
IF 3.6 3区 计算机科学 Q1 Social Sciences Pub Date : 2023-04-01 Epub Date: 2022-07-11 DOI: 10.1007/s11257-022-09337-8
Sooyeon Jeong, Laura Aymerich-Franch, Kika Arias, Sharifa Alghowinem, Agata Lapedriza, Rosalind Picard, Hae Won Park, Cynthia Breazeal

Despite the increase in awareness and support for mental health, college students' mental health is reported to decline every year in many countries. Several interactive technologies for mental health have been proposed and are aiming to make therapeutic service more accessible, but most of them only provide one-way passive contents for their users, such as psycho-education, health monitoring, and clinical assessment. We present a robotic coach that not only delivers interactive positive psychology interventions but also provides other useful skills to build rapport with college students. Results from our on-campus housing deployment feasibility study showed that the robotic intervention showed significant association with increases in students' psychological well-being, mood, and motivation to change. We further found that students' personality traits were associated with the intervention outcomes as well as their working alliance with the robot and their satisfaction with the interventions. Also, students' working alliance with the robot was shown to be associated with their pre-to-post change in motivation for better well-being. Analyses on students' behavioral cues showed that several verbal and nonverbal behaviors were associated with the change in self-reported intervention outcomes. The qualitative analyses on the post-study interview suggest that the robotic coach's companionship made a positive impression on students, but also revealed areas for improvement in the design of the robotic coach. Results from our feasibility study give insight into how learning users' traits and recognizing behavioral cues can help an AI agent provide personalized intervention experiences for better mental health outcomes.

尽管人们对心理健康的认识和支持不断提高,但在许多国家,大学生的心理健康状况却逐年下降。目前已经提出了几种心理健康互动技术,旨在使治疗服务更容易获得,但大多数技术只能为用户提供单向的被动内容,如心理教育、健康监测和临床评估。我们介绍的机器人教练不仅能提供互动式积极心理学干预,还能提供其他有用的技能,与大学生建立融洽的关系。我们在校园宿舍部署的可行性研究结果表明,机器人干预与学生心理健康、情绪和改变动机的提高有显著关联。我们还发现,学生的个性特征与干预结果、他们与机器人的工作联盟以及他们对干预的满意度都有关联。此外,学生与机器人之间的合作关系还与他们在改善健康状况的动机方面的前后变化有关。对学生行为线索的分析表明,一些语言和非语言行为与自我报告的干预结果的变化有关。对研究后访谈的定性分析表明,机器人教练的陪伴给学生留下了积极的印象,但也揭示了机器人教练设计中需要改进的地方。我们的可行性研究结果让我们深入了解了学习用户特征和识别行为线索如何帮助人工智能代理提供个性化的干预体验,从而获得更好的心理健康效果。
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引用次数: 0
Influence of Device Performance and Agent Advice on User Trust and Behaviour in a Care-taking Scenario 护理场景中设备性能和代理建议对用户信任和行为的影响
IF 3.6 3区 计算机科学 Q1 Social Sciences Pub Date : 2023-03-30 DOI: 10.1007/s11257-023-09357-y
Ingrid Zukerman, Andisheh Partovi, J. Hohwy
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引用次数: 0
Clustering of conversational bandits with posterior sampling for user preference learning and elicitation 用于用户偏好学习和启发的后验抽样会话土匪聚类
IF 3.6 3区 计算机科学 Q1 Social Sciences Pub Date : 2023-03-06 DOI: 10.1007/s11257-023-09358-x
Qizhi Li, Canzhe Zhao, Tong Yu, Junda Wu, Shuai Li
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引用次数: 0
Acknowledgment to reviewers 评审员致谢
IF 3.6 3区 计算机科学 Q1 Social Sciences Pub Date : 2023-02-01 DOI: 10.1007/s11257-009-9070-8
F. Bohnert, C. Busso, C. Conati, Ella Haig, M. Desmarais, V. Dimitrova, H. Dybkjær, D. Heckmann, V. Hollink, V. L. Jaquero, Patrick Jermann, S. McNee, C. Papatheodorou, Rupa Parameswaran, Dimitris Pierrakos, P. Pu, C. Rich, D. Rosaci, Anthony Savidis, S. Schiaffino, N. Sebe, Mayumi Ueda, D. Vogiatzis, Stefan Weibelzahl, Yi Zhang
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引用次数: 0
Acknowledgment to reviewers 评审员致谢
IF 3.6 3区 计算机科学 Q1 Social Sciences Pub Date : 2023-02-01 DOI: 10.1007/s11257-017-9197-y
T. Hammond, Simon Harper, Christoffer Holmgård, R. Jäschke, Markus Kächele, Helena Lindgren, G. Miklau, Yashar Moshfeghi, Radek Pelánek, Sergey Sosnovsky, M. Specht, Stephan Weibelzahl, M. Yudelson
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引用次数: 0
Use of topical and temporal profiles and their hybridisation for content-based recommendation 在基于内容的推荐中使用主题和时间概况及其混合
IF 3.6 3区 计算机科学 Q1 Social Sciences Pub Date : 2023-01-23 DOI: 10.1007/s11257-022-09354-7
Luis M. de Campos, J. M. Fernández-Luna, J. Huete
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引用次数: 0
Enhancing user awareness on inferences obtained from fitness trackers data. 提高用户对从健身追踪器数据中获得的推论的认识。
IF 3.6 3区 计算机科学 Q1 Social Sciences Pub Date : 2023-01-17 DOI: 10.1007/s11257-022-09353-8
Alexia Dini Kounoudes, Georgia M Kapitsaki, Ioannis Katakis

In the IoT era, sensitive and non-sensitive data are recorded and transmitted to multiple service providers and IoT platforms, aiming to improve the quality of our lives through the provision of high-quality services. However, in some cases these data may become available to interested third parties, who can analyse them with the intention to derive further knowledge and generate new insights about the users, that they can ultimately use for their own benefit. This predicament raises a crucial issue regarding the privacy of the users and their awareness on how their personal data are shared and potentially used. The immense increase in fitness trackers use has further increased the amount of user data generated, processed and possibly shared or sold to third parties, enabling the extraction of further insights about the users. In this work, we investigate if the analysis and exploitation of the data collected by fitness trackers can lead to the extraction of inferences about the owners routines, health status or other sensitive information. Based on the results, we utilise the PrivacyEnhAction privacy tool, a web application we implemented in a previous work through which the users can analyse data collected from their IoT devices, to educate the users about the possible risks and to enable them to set their user privacy preferences on their fitness trackers accordingly, contributing to the personalisation of the provided services, in respect of their personal data.

在物联网时代,敏感和非敏感数据被记录并传输到多个服务提供商和物联网平台,目的是通过提供优质服务提高我们的生活质量。然而,在某些情况下,这些数据可能会被感兴趣的第三方获取,他们可以对这些数据进行分析,从而获得更多的知识,产生关于用户的新见解,并最终用于自身利益。这种困境提出了一个至关重要的问题,即用户的隐私以及他们对其个人数据如何被共享和潜在使用的认识。健身追踪器使用量的大幅增长进一步增加了用户数据的生成和处理量,并有可能被共享或出售给第三方,从而进一步了解用户。在这项工作中,我们研究了对健身追踪器收集的数据进行分析和利用是否可以提取有关用户日常活动、健康状况或其他敏感信息的推断。根据研究结果,我们利用隐私增强行动(PrivacyEnhAction)隐私工具(我们在以前的工作中实施过的一个网络应用程序,用户可以通过该工具分析从其物联网设备收集到的数据)来教育用户可能存在的风险,并使他们能够在其健身追踪器上设置相应的用户隐私偏好,从而在其个人数据方面为所提供服务的个性化做出贡献。
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引用次数: 0
How do item features and user characteristics affect users’ perceptions of recommendation serendipity? A cross-domain analysis 项目特征和用户特征如何影响用户对推荐偶然性的感知?跨领域分析
IF 3.6 3区 计算机科学 Q1 Social Sciences Pub Date : 2023-01-17 DOI: 10.1007/s11257-022-09356-5
Ningxia Wang, L. Chen
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引用次数: 1
What we see is what we do: a practical Peripheral Vision-Based HMM framework for gaze-enhanced recognition of actions in a medical procedural task 我们所看到的就是我们所做的:一个实用的基于外围视觉的HMM框架,用于在医疗程序任务中增强对动作的凝视识别
IF 3.6 3区 计算机科学 Q1 Social Sciences Pub Date : 2023-01-04 DOI: 10.1007/s11257-022-09352-9
F. Wang, Thomas Kreiner, Alexander Lutz, Q. Lohmeyer, M. Meboldt
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引用次数: 3
期刊
User Modeling and User-Adapted Interaction
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