Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Collaborative filtering recommender systems
The adaptive web
Towards a customization of rating scales in adaptive systems
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
Evaluating rating scales personality
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Inspectability and control in social recommenders
Proceedings of the sixth ACM conference on Recommender systems
Recommending additional study materials: binary ratings vis-à-vis five-star ratings
BCS-HCI '13 Proceedings of the 27th International BCS Human Computer Interaction Conference
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As showed in a previous work, different users show different preferences with respect to the rating scales to use for evaluating items in recommender systems. Thus in order to promote users' participation and satisfaction with recommender systems, we propose to allow users to choose the rating scales to use. Thus, recommender systems should be able to deal with ratings coming from heterogeneous scales in order to produce correct recommendations. In this paper we present two user studies that investigate the role of rating scales on user's rating behavior, showing that the rating scales have their own "personality" and mathematical normalization is not enough to cope with mapping among different rating scales.