  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.
