Consumer trust in an Internet store
Information Technology and Management
Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
More than the sum of its members: challenges for group recommender systems
Proceedings of the working conference on Advanced visual interfaces
Integrating tradeoff support in product search tools for e-commerce sites
Proceedings of the 6th ACM conference on Electronic commerce
Trust building with explanation interfaces
Proceedings of the 11th international conference on Intelligent user interfaces
Embedding Emotional Context in Recommender Systems
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Interaction design guidelines on critiquing-based recommender systems
User Modeling and User-Adapted Interaction
Acceptance issues of personality-based recommender systems
Proceedings of the third ACM conference on Recommender systems
Recommender Systems Handbook
Consumer decision making in knowledge-based recommendation
Journal of Intelligent Information Systems
Anchoring effects of recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Group decision support for requirements negotiation
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
How personality influences users' needs for recommendation diversity?
CHI '13 Extended Abstracts on Human Factors in Computing Systems
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Recommender systems have already proved to be valuable for coping with the information overload problem in several application domains. They provide people with suggestions for items which are likely to be of interest for them; hence, a primary function of recommender systems is to help people make good choices and decisions. However, most previous research has focused on recommendation techniques and algorithms, and less attention has been devoted to the decision making processes adopted by the users and possibly supported by the system. There is still a gap between the importance that the community gives to the assessment of recommendation algorithms and the current range of ongoing research activities concerning human decision making. Different decision-psychological phenomena can influence the decision making of users of recommender systems, and research along these lines is becoming increasingly important and popular. This special issue highlights how the coupling of recommendation algorithms with the understanding of human choice and decision making theory has the potential to benefit research and practice on recommender systems and to enable users to achieve a good balance between decision accuracy and decision effort.