User and item attributes are essential side-information; their interactions
(i.e., their co-occurrence in the sample data) can significantly enhance
prediction accuracy in various recommender systems. We identify two different
types of attribute interactions, inner interactions and cross interactions:
inner interactions are those between only user attributes or those between only
item attributes; cross interactions are those between user attributes and item
attributes. Existing models do not distinguish these two types of attribute
interactions, which may not be the most effective way to exploit the
information carried by the interactions. To address this drawback, we propose a
neural Graph Matching based Collaborative Filtering model (GMCF), which
effectively captures the two types of attribute interactions through modeling
and aggregating attribute interactions in a graph matching structure for
recommendation. In our model, the two essential recommendation procedures,
characteristic learning and preference matching, are explicitly conducted
through graph learning (based on inner interactions) and node matching (based
on cross interactions), respectively. Experimental results show that our model
outperforms state-of-the-art models. Further studies verify the effectiveness
of GMCF in improving the accuracy of recommendation.

该论文提出了一种基于神经图匹配的协同过滤模型 (GMCF)，用于通过建模和聚合属性之间的交互，在图匹配结构中有效捕获两种类型的属性交互（内部交互和交叉交互），同时明确进行特征学习和偏好匹配的推荐过程，实验证明该模型优于现有的最先进模型，并能提高推荐的准确性。

基于神经图匹配的协同过滤

Neural Graph Matching based Collaborative Filtering

In this work we present the novel ASTRID method for investigating which
attribute interactions classifiers exploit when making predictions. Attribute
interactions in classification tasks mean that two or more attributes together
provide stronger evidence for a particular class label. Knowledge of such
interactions makes models more interpretable by revealing associations between
attributes. This has applications, e.g., in pharmacovigilance to identify
interactions between drugs or in bioinformatics to investigate associations
between single nucleotide polymorphisms. We also show how the found attribute
partitioning is related to a factorisation of the data generating distribution
and empirically demonstrate the utility of the proposed method.

介绍了一种新的方法 Astrid，可以用于研究分类器在进行预测时利用哪些属性交互作用，以提高模型解释性。该方法具有在药品监测和生物信息学中识别药物交互作用和探究单核苷酸多态性关联等应用价值。