GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Mixtures of distance-based models for ranking data
Computational Statistics & Data Analysis
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Generalized Bradley-Terry Models and Multi-Class Probability Estimates
The Journal of Machine Learning Research
Efficient Clustering for Orders
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Cluster analysis of heterogeneous rank data
Proceedings of the 24th international conference on Machine learning
Matchbox: large scale online bayesian recommendations
Proceedings of the 18th international conference on World wide web
Item Preference Parameters from Grouped Ranking Observations
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Bayesian inference for Plackett-Luce ranking models
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
An estimation of generalized bradley-terry models based on the em algorithm
Neural Computation
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Given a set of rating data for a set of items, determining preference levels of items is a matter of importance. Various probability models have been proposed to solve this task. One such model is the Plackett-Luce model, which parameterizes the preference level of each item by a real value. In this letter, the Plackett-Luce model is generalized to cope with grouped ranking observations such as movie or restaurant ratings. Since it is difficult to maximize the likelihood of the proposed model directly, a feasible approximation is derived, and the em algorithm is adopted to find the model parameter by maximizing the approximate likelihood which is easily evaluated. The proposed model is extended to a mixture model, and two applications are proposed. To show the effectiveness of the proposed model, numerical experiments with real-world data are carried out.