Serialized Interacting Mixed Membership Stochastic Block Model
This work provides a more flexible and powerful modeling approach for discrete recommendation problems, though it is incremental as it builds upon and generalizes prior stochastic block models.
The authors introduced the Serialized Interacting Mixed Membership Stochastic Block Model (SIMSBM), a unified framework that generalizes existing stochastic block models for recommender systems by handling arbitrarily large contexts and high-order interactions, and demonstrated improved predictive performance on six real-world datasets.
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.