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@article{guimera_predicting_2012,
	title = {Predicting {Human} {Preferences} {Using} the {Block} {Structure} of {Complex} {Social} {Networks}},
	volume = {7},
	url = {http://dx.doi.org/10.1371/journal.pone.0044620},
	doi = {10.1371/journal.pone.0044620},
	abstract = {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.},
	number = {9},
	urldate = {2014-10-04},
	journal = {PLoS One},
	author = {Guimerà, Roger and Llorente, Alejandro and Moro, Esteban and Sales-Pardo, Marta},
	month = sep,
	year = {2012},
	pages = {e44620},
}