Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Crowdsourcing user studies with Mechanical Turk
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Opinion space: a scalable tool for browsing online comments
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning with personalized recommender systems: A psychological view
Computers in Human Behavior
To switch or not to switch: understanding social influence in online choices
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Facilitating natural flow of information among "taste-based" groups
CHI '13 Extended Abstracts on Human Factors in Computing Systems
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Recommender systems have their origin in e-commerce. In this domain the users are meant to like the recommended information. This preference-consistency is not adequate or even desirable for all domains where recommender systems are implemented. One key issue for opinion formation and informed decision making is to be aware of more than one's own perspective. However, information search is often biased, because confirming information is favored over opposing information. Therefore it would be useful to recommend information that is inconsistent to users' prior perspective to help overcome this bias. The present paper deals with an online experiment aimed at investigating the effects of preference-consistent compared to preference-inconsistent recommendations on information selection and evaluation. Results showed a significant reduction of confirmation bias in the condition with preference-inconsistent recommendations. However, participants prefer preference-consistent recommendations in terms of global, cognitive and affective evaluations. We discuss the impact of these findings for application.