Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Powerful and consistent analysis of likert-type ratingscales
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A visitor's guide in an active museum: Presentations, communications, and reflection
Journal on Computing and Cultural Heritage (JOCCH)
The impact of rating scales on user's rating behavior
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Towards a customization of rating scales in adaptive systems
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Rating Bias and Preference Acquisition
ACM Transactions on Interactive Intelligent Systems (TiiS)
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User ratings are a valuable source of information for recommender systems: often, personalized suggestions are generated by predicting the user's preference for an item, based on ratings users explicitly provided for other items. In past experiments that were carried out by us in the gastronomy domain, results showed that rating scales have their own "personality" exerting an influence on user ratings. In this paper, we aim at deepening our knowledge of the effect of rating scale personality on user ratings by taking into account new empirical settings and a different domain (a museum), and partially different rating scales. We compare the results of these new experiments with our previous ones. Our aim is to further validate in a different application context, and domain, and with different rating scales, the fact that rating scales have their own personality which affects users' rating behavior.