An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A study of reviews and ratings on the internet
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Plot-polling: Collaborative Knowledge Visualization for Online Discussions
IV '06 Proceedings of the conference on Information Visualization
Design parameters of rating scales for web sites
ACM Transactions on Computer-Human Interaction (TOCHI)
Rating, voting & ranking: designing for collaboration & consensus
CHI '07 Extended Abstracts on Human Factors in Computing Systems
Scented Widgets: Improving Navigation Cues with Embedded Visualizations
IEEE Transactions on Visualization and Computer Graphics
A review of overview+detail, zooming, and focus+context interfaces
ACM Computing Surveys (CSUR)
I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Rate it again: increasing recommendation accuracy by user re-rating
Proceedings of the third ACM conference on Recommender systems
An Affective Interface for Conveying User Feedback
UKSIM '10 Proceedings of the 2010 12th International Conference on Computer Modelling and Simulation
An economic model of user rating in an online recommender system
UM'05 Proceedings of the 10th international conference on User Modeling
Rating support interfaces to improve user experience and recommender accuracy
Proceedings of the 7th ACM conference on Recommender systems
Rethinking the peer review process
Proceedings of the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing
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Rating interfaces are widely used on the Internet to elicit people's opinions. Little is known, however, about the effectiveness of these interfaces and their design space is relatively unexplored. We provide a taxonomy for the design space by identifying two axes: Measurement Scale for absolute rating vs. relative ranking, and Recall Support for the amount of information provided about previously recorded opinions. We present an exploration of the design space through iterative prototyping of three alternative interfaces and their evaluation. Among many findings, the study showed that users do take advantage of recall support in interfaces, preferring those that provide it. Moreover, we found that designing ranking systems is challenging; there may be a mismatch between a ranking interface that forces people to specify a total ordering for a set of items, and their mental model that some items are not directly comparable to each other.