Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Collaborative filtering: supporting social navigation in large, crowded infospaces
Designing information spaces
International Journal of Learning Technology
A descriptive model of information problem solving while using internet
Computers & Education
I will do it, but i don't like it: user reactions to preference-inconsistent recommendations
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning with personalized recommender systems: A psychological view
Computers in Human Behavior
Dealing with conflicting information from multiple nonlinear texts: Effects of prior attitudes
Computers in Human Behavior
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When a diversity of viewpoints on controversial issues is available, learners prefer information that is consistent with their prior preferences. Following previous findings, preference-inconsistent recommendations can be used to overcome this bias. Two studies investigated the role of two potential moderators (prior knowledge; cooperation vs. competition) that impact the influence of recommendations on confirmation bias (the tendency to select more preference-consistent information) and evaluation bias (the tendency to evaluate preference-consistent information as better). In Study 1, preference-inconsistent recommendations reduced confirmation bias irrespective of prior knowledge, whereas evaluation bias was only reduced for participants with no prior knowledge. In Study 2, it was found that preference-inconsistent recommendations led to reduced confirmation bias under cooperation and under competition, whereas evaluation bias was only reduced under cooperation. Together, these studies showed that preference-inconsistent recommendations have the potential to trigger critical thinking patterns under favorable conditions. Future research and practical implications are discussed.