Article Preview
TopIntroduction
Rapid developments in machine learning and computer vision technologies have enabled the use of artificial intelligence (AI) for visual clothing recommendation services (e.g., Thomassey & Zeng, 2018; Wong, 2018). Prior to these AI-based recommendation services, most professional clothing recommendations were typically provided by human fashion experts. Besides, the knowledge of AI recommendation services are collected and learnt from experts’ experience and knowledge of clothing (Yang et al., 2018). Even though AI can work in a manner like an expert does, users’ acceptance and subjective attitudes (e.g., perceived usefulness, perceived ease of use, and usage intention) toward these two recommendation services (AI and human experts) might differ.
Users’ subjective attitudes towards recommendation sources is related to the perceived characteristics of the sources. Previous studies found a recommendation source perceived to have both trustworthiness and expertise would generate the greatest acceptance and decision changes (Pornpitakpan, 2004; Stiff & Mongeau, 2016). However, it is unknown that whether users perceived AI with similar expertise and trustworthiness as human experts. Moreover, actual use of a recommendation service by users will also influence their subjective attitudes. Actual use of such services can lead to more real and meaningful responses by users. To date, little research has been conducted to compare investigate similarities and differences between AI and human expert recommendation services. Based on the information above, this study tried to explore (1) users’ acceptance and (2) perceived trustworthiness and expertise of AI and human expert recommendation service; (3) effects of actual use on (1) and (2); (4) differences of users’ subjective attitudes to AI and human expert recommendation service after use.
This paper provides answers to these questions by two experiments, in which users input their personal clothing images and received recommendations in the forms of fashion street snaps (experiment one: fashion street snap experiment) and online product images (experiment two: online product recommendation experiment). Fashion street snaps are social media images of fashionable citizens and celebrities, whereas online product images are images from online stores. This study recommended two types of images in two experiments through social media to help users show their subjective attitudes based on actual use. This paper is organized as follows. Next, we would introduce related work and propose research questions. Then, the methods and results of the two experiments are presented. At last, we would discuss and conclude this paper.