Modeling feature interactions is of crucial significance to high-quality feature engineering on multifiled sparse data. At present, a series of state-of-the-art methods extract cross features in a rather implicit bitwise fashion and lack enough comprehensive and flexible competence of learning sophisticated interactions among different feature fields. In this article, we propose a new broad attentive graph fusion network (BaGFN) to better model high-order feature interactions in a flexible and explicit manner. On the one hand, we design an attentive graph fusion module to strengthen high-order feature representation under graph structure. The graph-based module develops a new bilinear-cross aggregation function to aggregate the graph node information, employs the self-attention mechanism to learn the impact of neighborhood nodes, and updates the high-order representation of features by multihop fusion steps. On the other hand, we further construct a broad attentive cross module to refine high-order feature interactions at a bitwise level. The optimized module designs a new broad attention mechanism to dynamically learn the importance weights of cross features and efficiently conduct the sophisticated high-order feature interactions at the granularity of feature dimensions. The final experimental results demonstrate the effectiveness of our proposed model.

中文翻译：

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BaGFN：用于高阶特征交互的广泛关注图融合网络


特征交互建模对于多文件稀疏数据的高质量特征工程至关重要。目前，一系列最先进的方法以相当隐式的按位方式提取交叉特征，缺乏足够全面和灵活的学习不同特征字段之间复杂交互的能力。在本文中，我们提出了一种新的广泛关注图融合网络（BaGFN），以灵活、明确的方式更好地建模高阶特征交互。一方面，我们设计了一个注意力图融合模块来加强图结构下的高阶特征表示。基于图的模块开发了一种新的双线性交叉聚合函数来聚合图节点信息，采用自注意力机制来学习邻域节点的影响，并通过多跳融合步骤更新特征的高阶表示。另一方面，我们进一步构建了一个广泛的注意力交叉模块，以按位级别细化高阶特征交互。优化后的模块设计了一种新的广泛关注机制，可以动态学习交叉特征的重要性权重，并在特征维度的粒度上高效地进行复杂的高阶特征交互。最终的实验结果证明了我们提出的模型的有效性。