Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Identifying Users From Their Rating Patterns   This paper reports on our analysis of the 2011 CAMRa Challenge dataset (Track
2) for context-aware movie recommendation systems. The train dataset comprises
4,536,891 ratings provided by 171,670 users on 23,974$ movies, as well as the
household groupings of a subset of the users. The test dataset comprises 5,450
ratings for which the user label is missing, but the household label is
provided. The challenge required to identify the user labels for the ratings in
the test set. Our main finding is that temporal information (time labels of the
ratings) is significantly more useful for achieving this objective than the
user preferences (the actual ratings). Using a model that leverages on this
fact, we are able to identify users within a known household with an accuracy
of approximately 96% (i.e. misclassification rate around 4%).

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The categories of this paper are: mtrl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-th
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The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: atom-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ph
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The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: q-bio
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The categories of this paper are: supr-con
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The categories of this paper are: chao-dyn