Right invariant metrics and measures of presortedness
Discrete Applied Mathematics
What do we know about the metropolis algorithm?
Journal of Computer and System Sciences
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Mixtures of distance-based models for ranking data
Computational Statistics & Data Analysis
Cranking: Combining Rankings Using Conditional Probability Models on Permutations
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
SIAM Journal on Discrete Mathematics
Proceedings of the 16th international conference on World Wide Web
Cluster analysis of heterogeneous rank data
Proceedings of the 24th international conference on Machine learning
Unsupervised rank aggregation with distance-based models
Proceedings of the 25th international conference on Machine learning
Query dependent ranking using K-nearest neighbor
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Experiments in domain adaptation for statistical machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Tag recommendation for georeferenced photos
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
The Journal of Machine Learning Research
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Consider the setting where a panel of judges is repeatedly asked to (partially) rank sets of objects according to given criteria, and assume that the judges' expertise depends on the objects' domain. Learning to aggregate their rankings with the goal of producing a better joint ranking is a fundamental problem in many areas of Information Retrieval and Natural Language Processing, amongst others. However, supervised ranking data is generally difficult to obtain, especially if coming from multiple domains. Therefore, we propose a framework for learning to aggregate votes of constituent rankers with domain specific expertise without supervision. We apply the learning framework to the settings of aggregating full rankings and aggregating top-k lists, demonstrating significant improvements over a domain-agnostic baseline in both cases.