Personalising Explainable Recommendations: Literature and Conceptualisation
Personalising Explainable Recommendations: Literature and Conceptualisation
复制标题
个性化可解释的建议：文献和概念化
DOI：
10.1007/978-3-030-45691-7_49
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Raian Ali
中科院分区：
文献类型：
--
作者：
Mohammad Naiseh;Nan Jiang;Jianbing Ma;Raian Ali
智能系统中的解释旨在增强用户对其推理过程以及由此产生的决策和建议的理解。解释通常会增加信任、用户接受度和保留率。由于公众对人工智能的日益关注以及新法律的出现，例如欧洲的《通用数据保护条例》（GDPR），对解释的需求正在增加。然而，用户对解释的需求是不同的，并且这种需求可能取决于他们的动态上下文。解释可能会被视为信息过载，这使得个性化变得更加必要。在本文中，我们回顾了有关智能系统中个性化解释的文献。我们综合了一个概念，将被认为对个性化需求和实施很重要的各个方面结合在一起。此外，我们还发现了一些需要更多研究的挑战，包括解释的频率及其与持续用户体验的演变。
Explanations in intelligent systems aim to enhance a users’ understandability of their reasoning process and the resulted decisions and recommendations. Explanations typically increase trust, user acceptance and retention. The need for explanations is on the rise due to the increasing public concerns about AI and the emergence of new laws, such as the General Data Protection Regulation (GDPR) in Europe. However, users are different in their needs for explanations, and such needs can depend on their dynamic context. Explanations suffer the risk of being seen as information overload, and this makes personalisation more needed. In this paper, we review literature around personalising explanations in intelligent systems. We synthesise a conceptualisation that puts together various aspects being considered important for the personalisation needs and implementation. Moreover, we identify several challenges which would need more research, including the frequency of explanation and their evolution in tandem with the ongoing user experience.
使用交互式开放学习者模型解释基于需求的教育建议
DOI：
10.1145/3314183.3323463
发表时间：
2019
期刊：
UMAP '19
影响因子：
--
作者：
Barria-Pineda, Jordan;Akhuseyinoglu, Kamil;Brusilovsky, Peter
通讯作者：
Brusilovsky, Peter
Glass-Box：通过与支持语音的虚拟助理对话，用反事实陈述解释人工智能决策
DOI：
10.24963/ijcai.2018/865
发表时间：
2018
期刊：
--
影响因子：
--
作者：
Sokol K
通讯作者：
Sokol K