Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixtures of distance-based models for ranking data
Computational Statistics & Data Analysis
Fast nonparametric clustering with Gaussian blurring mean-shift
ICML '06 Proceedings of the 23rd international conference on Machine learning
Cluster analysis of heterogeneous rank data
Proceedings of the 24th international conference on Machine learning
Journal of Artificial Intelligence Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixtures of weighted distance-based models for ranking data with applications in political studies
Computational Statistics & Data Analysis
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This paper presents a statistical model for expressing preferences through rankings, when the number of alternatives (items to rank) is large. A human ranker will then typically rank only the most preferred items, and may not even examine the whole set of items, or know how many they are. Similarly, a user presented with the ranked output of a search engine, will only consider the highest ranked items. We model such situations by introducing a stagewise ranking model that operates with finite ordered lists called top-t orderings over an infinite space of items. We give algorithms to estimate this model from data, and demonstrate that it has sufficient statistics, being thus an exponential family model with continuous and discrete parameters. We describe its conjugate prior and other statistical properties. Then, we extend the estimation problem to multimodal data by introducing an Exponential-Blurring-Mean-Shift nonparametric clustering algorithm. The experiments highlight the properties of our model and demonstrate that infinite models over permutations can be simple, elegant and practical.