论文标题
序列化相互作用的混合成员随机块模型
Serialized Interacting Mixed Membership Stochastic Block Model
论文作者
论文摘要
去年，在推荐系统中使用随机块建模（SBM）的使用引起了人们的兴趣。这些模型被视为能够处理标记数据的张量分解技术的灵活替代方法。最近提出的著作建议通过将较大的上下文视为输入数据，并通过在上下文相关元素之间添加二阶交互来解决通过SBM解决离散建议问题。在这项工作中，我们表明这些模型都是单个全局框架的特殊情况：序列化相互作用的混合成员随机块模型（SIMSBM）。它允许建模任意较大的上下文以及任意高度的交互作用。我们证明了SIMSBM概括了一些最近基于SBM的基线。此外，我们证明我们的配方允许在六个现实世界数据集上增加预测能力。
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.
