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By offering consumers more proactive and individualized information services, recommender systems have proven to be a significant answer to the problem of information overload. And collaborative filtering approaches have proven to be an important component of many such recommender systems, allowing for the development of high-quality recommendations by harnessing the preferences of communities of similar users. In this study, we argue that individual personality traits may play an essential role in ensuring general end-user confidence in recommender systems. Individual elements to be examined include social suspiciousness as a personality feature and Daniel Kahneman’s fast and slow thinking styles, which distinguish between two modes of thought: System 1 is rapid, instinctual, and emotive, while System 2 is slower, more deliberate, and more rational. Due to a lack of explanation or inaccurate recommendation results, users may not trust current recommender systems, which poses a significant challenge for those systems. As such, having a trustworthy recommender system is essential. Three different trust-aware recommender system types are analyzed systematically in this study: robust systems that filter misleading information such as spam and fake news; social aware conscious systems that benefit from users’ social connections; and explainable systems that offer justifications for recommended products. On a sample size of 487 Romanian respondents, in an online survey, we have analyzed the impact of individual factors on the trustworthiness of the three types of recommender systems. Results show that fast thinking fully mediates the relationship between social suspiciousness and trust in all three types of recommender systems.

eISSN:
2558-9652
Langue:
Anglais