Clustering and Inference From Pairwise Comparisons
نویسندگان
چکیده
منابع مشابه
Efficient Ranking from Pairwise Comparisons
The ranking of n objects based on pairwise comparisons is a core machine learning problem, arising in recommender systems, ad placement, player ranking, biological applications and others. In many practical situations the true pairwise comparisons cannot be actively measured, but a subset of all n(n−1)/2 comparisons is passively and noisily observed. Optimization algorithms (e.g., the SVM) coul...
متن کاملRecommendation from Intransitive Pairwise Comparisons
In this paper we propose a full Bayesian probabilistic method to learn preferences from non-transitive pairwise comparison data. Such lack of transitivity easily arises when the number of pairwise comparisons is large, and they are given sequentially without allowing for consistency check. We develop a Bayesian Mallows model able to handle such data through a latent layer of uncertainty which c...
متن کاملApproximate Ranking from Pairwise Comparisons
A common problem in machine learning is to rank a set of n items based on pairwise comparisons. Here ranking refers to partitioning the items into sets of pre-specified sizes according to their scores, which includes identification of the top-k items as the most prominent special case. The score of a given item is defined as the probability that it beats a randomly chosen other item. Finding an...
متن کاملPassive and Active Ranking from Pairwise Comparisons
In the problem of ranking from pairwise comparisons, the learner has access to pairwise preferences among n objects and is expected to output a total order of these objects. This problem has a wide range of applications not only in computer science but also in other areas such as social science and economics. In this report, we will give a survey of passive and active learning algorithms for ra...
متن کاملRank Centrality: Ranking from Pairwise Comparisons
The question of aggregating pairwise comparisons to obtain a global ranking over a collection of objects has been of interest for a very long time: be it ranking of online gamers (e.g. MSR’s TrueSkill system) and chess players, aggregating social opinions, or deciding which product to sell based on transactions. In most settings, in addition to obtaining a ranking, finding ‘scores’ for each obj...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ACM SIGMETRICS Performance Evaluation Review
سال: 2015
ISSN: 0163-5999
DOI: 10.1145/2796314.2745887