The Influence of Trust Cues on the Trustworthiness of Online Reviews for Recommendations

Barbu, C.-M., Carbonell, G., & Ziegler, J. (2019). Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 1687–1689.

Abstract

In recent years, recommender systems have started to exploit user-generated content, in particular online reviews, as an additional means of personalizing and explaining their predictions. However, reviews that are poorly written or perceived as fake may have a detrimental effect on the users’ trust in the recommendations. Embedding so-called “trust cues” in the user interface is a technique that can help users judge the trustworthiness of presented information. We report preliminary results from an online user study that investigated the impact of trust cues—in the form of helpfulness votes—on the trustworthiness of online reviews for recommendations.

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