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.

翻译：在过去几年里,人们重新对在推荐者系统中使用随机区块建模(SBM)感兴趣,这些模型被视为一种灵活的替代方法,可以替代能够处理标签数据的高压分解技术。最近提出的通过SBM处理离散的建议问题,将大背景视为输入数据,并增加环境相关要素之间的第二顺序互动。在这项工作中,我们表明这些模型都是单一全球框架的特殊情况:串列式间混合成员结构块建模模型(SIMSBM),它可以模拟一个任意大的环境以及任意高层次的互动。我们证明SIMSBM概括了最近的基于SBM的基线。此外,我们证明我们的配方能够增加六个真实世界数据集的预测力。