# Accurate and scalable social recommendation using mixed-membership stochastic block models

arXiv: https://arxiv.org/abs/1604.01170 · HTML: https://mlsanity.com/paper/1604.01170/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2016.

## Built on by (most cited first, 5 of 5)

- [Powered Dirichlet Process for Controlling the Importance of "Rich-Get-Richer" Prior Assumptions in Bayesian Clustering](https://mlsanity.com/paper/2104.12485/) (2021)
- [The maximum capability of a topological feature in link prediction](https://mlsanity.com/paper/2206.15101/) (2022)
- [Node metadata can produce predictability transitions in network inference problems](https://mlsanity.com/paper/2103.14424/) (2021)
- [Serialized Interacting Mixed Membership Stochastic Block Model](https://mlsanity.com/paper/2209.07813/) (2022)
- [Dynamic Mixed Membership Stochastic Block Model for Weighted Labeled Networks](https://mlsanity.com/paper/2304.05894/) (2023)

## Abstract (arXiv)

With ever-increasing amounts of online information available, modeling and predicting individual preferences-for books or articles, for example-is becoming more and more important. Good predictions enable us to improve advice to users, and obtain a better understanding of the socio-psychological processes that determine those preferences. We have developed a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of individuals' preferences. Our approach is based on the explicit assumption that there are groups of individuals and of items, and that the preferences of an individual for an item are determined only by their group memberships. Importantly, we allow each individual and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches, such as matrix factorization, we do not assume implicitly or explicitly that individuals in each group prefer items in a single group of items. The resulting overlapping groups and the predicted preferences can be inferred with a expectation-maximization algorithm whose running time scales linearly (per iteration). Our approach enables us to predict individual preferences in large datasets, and is considerably more accurate than the current algorithms for such large datasets.