Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scalable Bayesian Preference Learning for Crowds   We propose a scalable Bayesian preference learning method for jointly
predicting the preferences of individuals as well as the consensus of a crowd
from pairwise labels. Peoples' opinions often differ greatly, making it
difficult to predict their preferences from small amounts of personal data.
Individual biases also make it harder to infer the consensus of a crowd when
there are few labels per item. We address these challenges by combining matrix
factorisation with Gaussian processes, using a Bayesian approach to account for
uncertainty arising from noisy and sparse data. Our method exploits input
features, such as text embeddings and user metadata, to predict preferences for
new items and users that are not in the training set. As previous solutions
based on Gaussian processes do not scale to large numbers of users, items or
pairwise labels, we propose a stochastic variational inference approach that
limits computational and memory costs. Our experiments on a recommendation task
show that our method is competitive with previous approaches despite our
scalable inference approximation. We demonstrate the method's scalability on a
natural language processing task with thousands of users and items, and show
improvements over the state of the art on this task. We make our software
publicly available for future work.

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The categories of this paper are: mtrl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: physics
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The categories of this paper are: eess
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-th
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The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: gr-qc
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The categories of this paper are: q-fin
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The categories of this paper are: nlin
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The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
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The categories of this paper are: adap-org
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The categories of this paper are: math