Recommendation from Intransitive Pairwise Comparisons
نویسندگان
چکیده
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 captures the generation of preference misreporting. We then construct an MCMC algorithm, and test the procedure on simulated data.
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