Serialized Interacting Mixed Membership Stochastic Block Model
Not on the map. Why?
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 30 references that are in the map. First posted on arXiv 16 Sep 2022.
Builds on
6 of 30 references.
References · 6
- 2020Interactions in information spread: quantification and interpretation using stochastic block modelscited by 7
- 2016Accurate and scalable social recommendation using mixed-membership stochastic block modelscited by 5
- 2020Optimal prediction of decisions and model selection in social dilemmas using block modelscited by 5
- 2018Tensorial and bipartite block models for link prediction in layered networks and temporal networkscited by 4
- 2021Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategiescited by 3
- 2015Long-term evolution of email networks: Statistical regularities, predictability and stability of social behaviorscited by 1
Built on by
Not cited yet.