Predicting human preferences using the block structure of complex social networks

TL;DR

This paper introduces a Bayesian stochastic block model approach to predict individual preferences in social networks, outperforming existing recommender systems and revealing group-based preference patterns.

Contribution

The paper presents a novel Bayesian stochastic block model method for preference prediction that improves accuracy and provides insights into social group preferences.

Findings

Achieves 38-99% better accuracy than industry algorithms.

Identifies groups with similar preferences in social networks.

Enables analysis of group characteristics and decision-making processes.

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.