The Influence of Personality Traits on User Interaction with Recommendation Interfaces

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2023-03-10 DOI:https://dl.acm.org/doi/10.1145/3558772
Dongning Yan, Li Chen
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Abstract

Users’ personality traits can take an active role in affecting their behavior when they interact with a computer interface. However, in the area of recommender systems (RS), though personality-based RS has been extensively studied, most works focus on algorithm design, with little attention paid to studying whether and how the personality may influence users’ interaction with the recommendation interface. In this manuscript, we report the results of a user study (with 108 participants) that not only measured the influence of users’ personality traits on their perception and performance when using the recommendation interface but also employed an eye-tracker to in-depth reveal how personality may influence users’ eye-movement behavior. Moreover, being different from related work that has mainly been conducted in a single product domain, our user study was performed in three typical application domains (i.e., electronics like smartphones, entertainment like movies, and tourism like hotels). Our results show that mainly three personality traits, i.e., Openness to experience, Conscientiousness, and Agreeableness, significantly influence users’ perception and eye-movement behavior, but the exact influences vary across the domains. Finally, we provide a set of guidelines that might be constructive for designing a more effective recommendation interface based on user personality.

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人格特质对用户与推荐界面交互的影响
当用户与计算机界面交互时,他们的个性特征会对他们的行为产生积极影响。然而,在推荐系统(RS)领域,虽然基于个性的推荐系统已经得到了广泛的研究,但大多数工作都集中在算法设计上,很少关注人格是否以及如何影响用户与推荐界面的交互。在本文中,我们报告了一项用户研究(108名参与者)的结果,该研究不仅测量了用户的人格特质对他们使用推荐界面时的感知和表现的影响,而且采用眼动仪深入揭示了人格如何影响用户的眼动行为。此外,与主要在单一产品领域进行的相关工作不同,我们的用户研究是在三个典型的应用领域进行的(即,电子产品,如智能手机,娱乐,如电影,旅游,如酒店)。研究结果表明,开放性、尽责性和亲和性对用户的感知和眼动行为有显著影响,但具体影响程度在不同领域有所不同。最后,我们提供了一组指导方针,这些指导方针可能有助于设计基于用户个性的更有效的推荐界面。
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CiteScore
7.20
自引率
4.30%
发文量
567
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