Learning the Compositional Visual Coherence for Complementary Recommendations

• URL: http://arxiv.org/abs/2006.04380v1
• Date: Mon, 8 Jun 2020 06:57:18 GMT
• Title: Learning the Compositional Visual Coherence for Complementary Recommendations
• Authors: Zhi Li, Bo Wu, Qi Liu, Likang Wu, Hongke Zhao, Tao Mei
• Abstract summary: Complementary recommendations aim at providing users product suggestions that are supplementary and compatible with their obtained items. We propose a novel Content Attentive Neural Network (CANN) to model the comprehensive compositional coherence on both global contents and semantic contents.
• Score: 62.60648815930101
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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