Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
A Mathematical Theory of Communication
A Mathematical Theory of Communication
An MDP-based recommender system
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Preference-based graphic models for collaborative filtering
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
A maximum entropy web recommendation system: combining collaborative and content features
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Preference networks: probabilistic models for recommendation systems
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Latent grouping models for user preference prediction
Machine Learning
Developing a fuzzy TOPSIS approach based on subjective weights and objective weights
Expert Systems with Applications: An International Journal
Two-Way Grouping by One-Way Topic Models
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Intelligent integrated data processing model for oceanic warning system
Knowledge-Based Systems
A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting
Expert Systems with Applications: An International Journal
An entropy-based neighbor selection approach for collaborative filtering
Knowledge-Based Systems
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Within the task of collaborative filtering two challenges for computing conditional probabilities exist. First, the amount of training data available is typically sparse with respect to the size of the domain. Thus, support for higher-order interactions is generally not present. Second, the variables that we are conditioning upon vary for each query. That is, users label different variables during each query. For this reason, there is no consistent input to output mapping. To address these problems we purpose a maximum entropy approach using a non-standard measure of entropy. This approach can be simplified to solving a set of linear equations that can be efficiently solved.