Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Workshop on human decision making in recommender systems: decisions@RecSys'13
Proceedings of the 7th ACM conference on Recommender systems
Human Decision Making and Recommender Systems
ACM Transactions on Interactive Intelligent Systems (TiiS)
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We explore how consumer preferences at the time of consumption are impacted by predictions generated by recommender systems. We conducted three controlled laboratory experiments to explore the effects of system recommendations on preferences. Results provide strong evidence that the rating provided by a recommender system serves as an anchor for the consumer's constructed preference. Consumer's preferences appear malleable and can be significantly influenced by the recommendation received. Additionally, the effects of pure number-based anchoring can be separated from the effects of the perceived reliability of a recommender system. In particular, when the recommender system was described to the participants as being in testing phase, the anchoring effect was reduced. Finally, the effect of anchoring is roughly continuous, operating over a range of perturbations of the system.