Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: User Bias Removal in Review Score Prediction   Review score prediction of text reviews has recently gained a lot of
attention in recommendation systems. A major problem in models for review score
prediction is the presence of noise due to user-bias in review scores. We
propose two simple statistical methods to remove such noise and improve review
score prediction. Compared to other methods that use multiple classifiers, one
for each user, our model uses a single global classifier to predict review
scores. We empirically evaluate our methods on two major categories
(\textit{Electronics} and \textit{Movies and TV}) of the SNAP published Amazon
e-Commerce Reviews data-set and Amazon \textit{Fine Food} reviews data-set. We
obtain improved review score prediction for three commonly used text feature
representations.

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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
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The categories of this paper are: econ
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
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The categories of this paper are: bayes-an
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The categories of this paper are: math-ph
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The categories of this paper are: stat
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The categories of this paper are: eess
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The categories of this paper are: patt-sol
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The categories of this paper are: ao-sci
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn