With increasing amounts of information available, modeling and predicting user preferences-for books or articles, for exampleare becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users' ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user's and item's groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets.recommender systems | stochastic block model | collaborative filtering | social recommendation | scalable algorithm T he goal of recommender systems is to predict what movies we are going to like, what books we are going to purchase, or even who we might be interested in dating. The rapidly growing amount of data on item reviews, ratings, and purchases from a growing number of online platforms holds the promise to facilitate the development of more informed models for recommendation. At the same time, however, it poses the challenge of developing algorithms that can handle such large amounts of data accurately and efficiently.A plausible expectation when developing recommendation algorithms is that similar users relate to similar items in similar ways; e.g., they purchase similar items and give the same item similar ratings. This means that we can use the rating history of a set of users to make recommendations, even without knowing anything about the characteristics of users or items. This is the basic underlying assumption of collaborative filtering, one of the most common approaches in recommender systems (1). However, most research in recommender systems has focused on the development of scalable algorithms, often at the price of implicitly using models that are overly simplistic or unrealistic. For example, matrix factorization and latent feature approaches assume that users and items live in an abstract low-dimensional space, but whether such a space is expressive enough to accommodate the rich variety of user behaviors is rarely discussed. As a result, many current approaches have significantly lower accuracies than inference approaches based on models of user preferences that are socially more realistic (2). On the other hand, these more realistic approaches do not scale well with dataset size, whic...

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Advancing our understanding of human behavior hinges on the ability of theories to unveil the mechanisms underlying such behaviors. Measuring the ability of theories and models to predict unobserved behaviors provides a principled method to evaluate their merit and, thus, to help establish which mechanisms are most plausible. Here, we propose models and develop rigorous inference approaches to predict strategic decisions in dyadic social dilemmas. In particular, we use bipartite stochastic block models that incorporate information about the dilemmas faced by individuals. We show, combining these models with empirical data on strategic decisions in dyadic social dilemmas, that individual strategic decisions are to a large extent predictable, despite not being "rational. " The analysis of these models also allows us to conclude that: (i) individuals do not perceive games according their game-theoretical structure; (ii) individuals make decisions using combinations of multiple simple strategies, which our approach reveals naturally.

In social networks, individuals constantly drop ties and replace them by new ones in a highly unpredictable fashion. This highly dynamical nature of social ties has important implications for processes such as the spread of information or of epidemics. Several studies have demonstrated the influence of a number of factors on the intricate microscopic process of tie replacement, but the macroscopic long-term effects of such changes remain largely unexplored. Here we investigate whether, despite the inherent randomness at the microscopic level, there are macroscopic statistical regularities in the long-term evolution of social networks. In particular, we analyze the email network of a large organization with over 1,000 individuals throughout four consecutive years. We find that, although the evolution of individual ties is highly unpredictable, the macro-evolution of social communication networks follows well-defined statistical patterns, characterized by exponentially decaying log-variations of the weight of social ties and of individuals’ social strength. At the same time, we find that individuals have social signatures and communication strategies that are remarkably stable over the scale of several years.

Population behavior, like voting and vaccination, depends on the structure of social networks. This structure can differ depending on behavior type and is typically hidden. However, we do often have behavioral data, albeit only snapshots taken at one time point. We present a method jointly inferring a model for both network structure and human behavior using only snapshot population-level behavioral data. This exploits the simplicity of a few parameter model, geometric sociodemographic network model, and a spin-based model of behavior. We illustrate, for the European Union referendum and two London mayoral elections, how the model offers both prediction and the interpretation of the homophilic inclinations of the population. Beyond extracting behavior-specific network structure from behavioral datasets, our approach yields a framework linking inequalities and social preferences to behavioral outcomes. We illustrate potential network-sensitive policies: How changes to income inequality, social temperature, and homophilic preferences might have reduced polarization in a recent election.