期刊
PLOS ONE
卷 7, 期 9, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0044620
关键词
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资金
- James S. McDonnell Foundation Research Award
- European Union [PIRG-GA-2010-277166, PIRG-GA-2010-268342]
- Spanish Ministerio de Economia y Competitividad [FIS2010-18639, FIS2006-01485, FIS2010-22047-C05-04]
- ICREA Funding Source: Custom
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.
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