# Serialized Interacting Mixed Membership Stochastic Block Model

arXiv: https://arxiv.org/abs/2209.07813 · HTML: https://mlsanity.com/paper/2209.07813/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [Interactions in information spread: quantification and interpretation using stochastic block models](https://mlsanity.com/paper/2004.04552/) (2020)
- [Accurate and scalable social recommendation using mixed-membership stochastic block models](https://mlsanity.com/paper/1604.01170/) (2016)
- [Optimal prediction of decisions and model selection in social dilemmas using block models](https://mlsanity.com/paper/2004.12151/) (2020)
- [Tensorial and bipartite block models for link prediction in layered networks and temporal networks](https://mlsanity.com/paper/1803.01616/) (2018)
- [Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies](https://mlsanity.com/paper/2103.06121/) (2021)
- [Long-term evolution of email networks: Statistical regularities, predictability and stability of social behaviors](https://mlsanity.com/paper/1506.01516/) (2015)

## Abstract (arXiv)

Last years have seen a regain of interest for the use of stochastic block modeling (SBM) in recommender systems. These models are seen as a flexible alternative to tensor decomposition techniques that are able to handle labeled data. Recent works proposed to tackle discrete recommendation problems via SBMs by considering larger contexts as input data and by adding second order interactions between contexts' related elements. In this work, we show that these models are all special cases of a single global framework: the Serialized Interacting Mixed membership Stochastic Block Model (SIMSBM). It allows to model an arbitrarily large context as well as an arbitrarily high order of interactions. We demonstrate that SIMSBM generalizes several recent SBM-based baselines. Besides, we demonstrate that our formulation allows for an increased predictive power on six real-world datasets.