Gabrielle Alves, Dietmar Jannach, Rodrigo Ferrari de Souza, Daniela Damian, Marcelo Garcia Manzato
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In this research, we explore to what extent digital nudging , i.e., subtly directing user choices in a specific direction, can help to raise the attention and interest of users for off-profile content. We conducted a user study ( $$N=1064$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1064</mml:mn> </mml:mrow> </mml:math> ) on a real-world social book recommendation app. We find that users who are nudged towards recommended books of their non-preferred genres significantly more often put these off-profile books on their reading lists, thus confirming the effectiveness of digital nudging in this application. However, we also found that digital nudges may negatively impact the users’ beliefs and attitudes towards the system and a more limited intention to use the system in the future. 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引用次数: 0
摘要
在推荐系统的许多应用领域中,例如在流媒体网站上,推荐服务提供商的一个主要目标是通过帮助用户发现他们喜欢的新类型的内容来增加用户的参与度。设计的标准协同过滤算法通常会导致一定程度的发现。尽管如此,在某些领域,更积极地向用户推广超越他们过去偏好的内容(“off-profile”)可能会有所帮助,从而帮助用户探索新内容。但是,当将这些非配置文件内容与更熟悉的内容结合在一起显示给用户时,新的内容项可能会被忽略。在这项研究中,我们探讨了数字推动,即在特定方向上巧妙地引导用户选择,可以在多大程度上帮助提高用户对非个人资料内容的关注和兴趣。我们在一个真实世界的社交图书推荐应用程序上进行了一项用户研究($$N=1064$$ N = 1064)。我们发现,那些被推荐他们不喜欢的类型的书的用户更经常把这些不喜欢的书放在他们的阅读清单上,从而证实了数字助推在这个应用程序中的有效性。然而,我们也发现,数字推动可能会对用户对系统的信念和态度产生负面影响,并在未来使用系统的意愿更有限。因此,我们发现,虽然推荐中的数字推动在短期内是有效的,但必须谨慎行事,密切关注用户对整体质量的看法和潜在的有害长期影响。
Digitally nudging users to explore off-profile recommendations: here be dragons
Abstract In many application domains of recommender systems, e.g., on media streaming sites, one main goal of the provider of the recommendation service is to increase the engagement of users by helping them discover new types of content they like. Standard collaborative filtering algorithms by design often lead to a certain level of discovery. Nonetheless, in certain domains, it may be helpful to more actively promote content to users beyond their past preference profile (“off-profile”) and thereby help users explore new content. However, when showing such off-profile content to users in combination with more familiar content, the new content items may be overlooked. In this research, we explore to what extent digital nudging , i.e., subtly directing user choices in a specific direction, can help to raise the attention and interest of users for off-profile content. We conducted a user study ( $$N=1064$$ N=1064 ) on a real-world social book recommendation app. We find that users who are nudged towards recommended books of their non-preferred genres significantly more often put these off-profile books on their reading lists, thus confirming the effectiveness of digital nudging in this application. However, we also found that digital nudges may negatively impact the users’ beliefs and attitudes towards the system and a more limited intention to use the system in the future. As a result, we find that digital nudging in recommendations, while effective in the short run, must be done with due care, keeping an eye on the overall quality perceptions by users and potentially harmful long-term effects.
期刊介绍:
User Modeling and User-Adapted Interaction provides an interdisciplinary forum for the dissemination of novel and significant original research results about interactive computer systems that can adapt themselves to their users, and on the design, use, and evaluation of user models for adaptation. The journal publishes high-quality original papers from, e.g., the following areas: acquisition and formal representation of user models; conceptual models and user stereotypes for personalization; student modeling and adaptive learning; models of groups of users; user model driven personalised information discovery and retrieval; recommender systems; adaptive user interfaces and agents; adaptation for accessibility and inclusion; generic user modeling systems and tools; interoperability of user models; personalization in areas such as; affective computing; ubiquitous and mobile computing; language based interactions; multi-modal interactions; virtual and augmented reality; social media and the Web; human-robot interaction; behaviour change interventions; personalized applications in specific domains; privacy, accountability, and security of information for personalization; responsible adaptation: fairness, accountability, explainability, transparency and control; methods for the design and evaluation of user models and adaptive systems