Serialized Interacting Mixed Membership Stochastic Block Model

TL;DR

This paper introduces the Serialized Interacting Mixed Membership Stochastic Block Model (SIMSBM), a comprehensive framework that generalizes existing SBM approaches to handle large contexts and high-order interactions, improving predictive accuracy in recommender systems.

Contribution

The paper presents SIMSBM, a unified framework that encompasses and extends previous SBM-based models for recommendation, enabling modeling of larger contexts and higher-order interactions.

Findings

SIMSBM generalizes several recent SBM-based models.

It demonstrates increased predictive power on six real-world datasets.

The framework effectively models large contexts and complex interactions.

Abstract

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.