GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Machine Learning
Modern Information Retrieval
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
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
New Recommendation Techniques for Multicriteria Rating Systems
IEEE Intelligent Systems
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Accuracy improvements for multi-criteria recommender systems
Proceedings of the 13th ACM Conference on Electronic Commerce
Combining user preferences and user opinions for accurate recommendation
Electronic Commerce Research and Applications
Group decision making model and approach based on interval preference orderings
Computers and Industrial Engineering
Accuracy of aggregate data in distributed project settings: Model, analysis and implications
Journal of Data and Information Quality (JDIQ)
A hidden Markov model for collaborative filtering
MIS Quarterly
Hybrid recommendation approaches for multi-criteria collaborative filtering
Expert Systems with Applications: An International Journal
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Collaborative filtering algorithms learn from the ratings of a group of users on a set of items to find personalized recommendations for each user. Traditionally they have been designed to work with one-dimensional ratings. With interest growing in recommendations based on multiple aspects of items, we present an algorithm for using multicomponent rating data. The presented mixture model-based algorithm uses the component rating dependency structure discovered by a structure learning algorithm. The structure is supported by the psychometric literature on the halo effect. This algorithm is compared with a set of model-based and instance-based algorithms for single-component ratings and their variations for multicomponent ratings. We evaluate the algorithms using data from Yahoo! Movies. Use of multiple components leads to significant improvements in recommendations. However, we find that the choice of algorithm depends on the sparsity of the training data. It also depends on whether the task of the algorithm is to accurately predict ratings or to retrieve relevant items. In our experiments a model-based multicomponent rating algorithm is able to better retrieve items when training data are sparse. However, if the training data are not sparse, or if we are trying to predict the rating values accurately, then the instance-based multicomponent rating collaborative filtering algorithms perform better. Beyond generating recommendations we show that the proposed model can fill in missing rating components. Theories in psychometric literature and the empirical evidence suggest that rating specific aspects of a subject is difficult. Hence, filling in the missing component values leads to the possibility of a rater support system to facilitate gathering of multicomponent ratings.