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Multi-modal Knowledge Graphs for Recommender Systems

Multi-modal Knowledge Graphs for Recommender SystemsRui Sun1 , Xuezhi Cao2, Yan Zhao3, Junchen Wan2, Kun Zhou4, Fuzheng Zhang2 Zhongyuan Wang2and Kai Zheng1 1 School of Computer Science and Engineering, University of Electronic Science and Technology of China2 Meituan-Dianping Group3 Aalborg University, Danmark4 School of Information, Renmin University of {caoxuezhi, zhangfuzheng, Systems have shown great potential to solve theinformation explosion problem and enhance user experience invarious online applications. To tackle data sparsity and cold startproblems in Recommender Systems , researchers propose knowledgegraphs (KGs) based recommendations by leveraging valuable exter-nal Knowledge as auxiliary information. However, most of theseworks ignore the variety of data types ( , texts and images) inmulti-modal Knowledge Graphs (MMKGs). In this paper, we proposeMulti-modal Knowledge graph Attention Network (MKGAT) tobetter enhance Recommender Systems by leveraging multi-modalknowledge.}

2.1 Multi-modal Knowledge Graphs Multi-modal Knowledge Graphs (MKGs) enriches the types of knowledge by introducing information of other modals into the tra-ditional KGs. Entity images or entity description could provide sig-nificant visual or textual information for knowledge representation learning.

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