Complementary recommendations, which aim at providing users product suggestions that are supplementary and compatible with their obtained items, have become a hot topic in both academia and industry in recent years. %However, it is challenging due to its complexity and subjectivity. Existing work mainly focused on modeling the co-purchased relations between two items, but the compositional associations of item collections are largely unexplored. Actually, when a user chooses the complementary items for the purchased products, it is intuitive that she will consider the visual semantic coherence (such as color collocations, texture compatibilities) in addition to global impressions. Towards this end, in this paper, we propose a novel Content Attentive Neural Network (CANN) to model the comprehensive compositional coherence on both global contents and semantic contents. Specifically, we first propose a \textit{Global Coherence Learning} (GCL) module based on multi-heads attention to model the global compositional coherence. Then, we generate the semantic-focal representations from different semantic regions and design a \textit{Focal Coherence Learning} (FCL) module to learn the focal compositional coherence from different semantic-focal representations. Finally, we optimize the CANN in a novel compositional optimization strategy. Extensive experiments on the large-scale real-world data clearly demonstrate the effectiveness of CANN compared with several state-of-the-art methods.

本文提出了一种新颖的内容注意力神经网络，可以模拟全局和语义结构的综合组成连贯性，并通过多个头部监督的全局连贯模块（GCL）和基于不同语义/关键区域的集中连贯学习模块（FCL）来学习集中连贯性，并在大规模真实世界数据上进行的实验表明，与几种最先进的方法相比，该方法的有效性得到清楚证明。

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