Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting human preferences using the block structure of complex social
  networks   With ever-increasing available data, predicting individuals' preferences and
helping them locate the most relevant information has become a pressing need.
Understanding and predicting preferences is also important from a fundamental
point of view, as part of what has been called a "new" computational social
science. Here, we propose a novel approach based on stochastic block models,
which have been developed by sociologists as plausible models of complex
networks of social interactions. Our model is in the spirit of predicting
individuals' preferences based on the preferences of others but, rather than
fitting a particular model, we rely on a Bayesian approach that samples over
the ensemble of all possible models. We show that our approach is considerably
more accurate than leading recommender algorithms, with major relative
improvements between 38% and 99% over industry-level algorithms. Besides, our
approach sheds light on decision-making processes by identifying groups of
individuals that have consistently similar preferences, and enabling the
analysis of the characteristics of those groups.

21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph