Predicting human preferences using the block structure of complex social networks

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.

References in corpus (6)

• The scaling laws of human travel
• Missing and spurious interactions and the reconstruction of complex networks
• Extracting the hierarchical organization of complex systems
• Information diffusion epidemics in social networks
• Phase transition in the detection of modules in sparse networks
• On Universality in Human Correspondence Activity

Cited by in corpus (17)

• Networks beyond pairwise interactions: structure and dynamics
• Efficiently inferring community structure in bipartite networks
• Bayesian stochastic blockmodeling
• Common neighbours and the local-community-paradigm for link prediction in bipartite networks
• Multilayer stochastic block models reveal the multilayer structure of complex networks
• A network inference method for large-scale unsupervised identification of novel drug-drug interactions
• Consistencies and inconsistencies between model selection and link prediction in networks
• Accurate and scalable social recommendation using mixed-membership stochastic block models
• Matching-centrality decomposition and the forecasting of new links in networks
• Predicting future conflict between team-members with parameter-free models of social networks
• Dynamic Patterns of Academic Forum Activities
• Interactions in information spread: quantification and interpretation using stochastic block models
• Cliques in complex networks reveal link formation and community evolution
• Hierarchical clustering of bipartite data sets based on the statistical significance of coincidences
• Node metadata can produce predictability transitions in network inference problems
• Powered Dirichlet Process for Controlling the Importance of "Rich-Get-Richer" Prior Assumptions in Bayesian Clustering
• Network-based models for social recommender systems