Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
A mathematical theory of communication
ACM SIGMOBILE Mobile Computing and Communications Review
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
Estimation of entropy and mutual information
Neural Computation
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
Detecting noise in recommender system databases
Proceedings of the 11th international conference on Intelligent user interfaces
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Tagommenders: connecting users to items through tags
Proceedings of the 18th international conference on World wide web
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
Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit
Proceedings of the fifth ACM conference on Recommender systems
Proceedings of the fifth ACM conference on Recommender systems
An economic model of user rating in an online recommender system
UM'05 Proceedings of the 10th international conference on User Modeling
Collaborative Filtering Recommender Systems
Foundations and Trends in Human-Computer Interaction
Rating support interfaces to improve user experience and recommender accuracy
Proceedings of the 7th ACM conference on Recommender systems
Combining prestige and relevance ranking for personalized recommendation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Most recommender systems assume user ratings accurately represent user preferences. However, prior research shows that user ratings are imperfect and noisy. Moreover, this noise limits the measurable predictive power of any recommender system. We propose an information theoretic framework for quantifying the preference information contained in ratings and predictions. We computationally explore the properties of our model and apply our framework to estimate the efficiency of different rating scales for real world datasets. We then estimate how the amount of information predictions give to users is related to the scale ratings are collected on. Our findings suggest a tradeoff in rating scale granularity: while previous research indicates that coarse scales (such as thumbs up / thumbs down) take less time, we find that ratings with these scales provide less predictive value to users. We introduce a new measure, preference bits per second, to quantitatively reconcile this tradeoff.