To address the following two problems of existing work for user identity linkage(UIL),one is that only text and net-work structure modal information is considered in most methods,more modal information such as images is ignored,the other is that multi-modal information is fused through simple characteristic concatenation,while the complementarity,correlation,and heterogeneity of different modal information is ignored,an UIL method based on multi-modal information fusion,named MIFUIL,was proposed.The embedding of text,visual and heterogeneous network modal information of the user was acquired.The attention mechanisms was used to learn the complementarity,correlation,and heterogeneity among different modal informa-tion to obtain the fused embedding of the user,thus identical user identities across platforms were linked through multi-modal contrastive learning.Extensive experiments on two real-world multi-modal datasets TWFQ and DB-YAGO demonstrate that MIFUIL significantly outperforms the state-of-art methods.
关键词
用户连接/用户身份识别/实体用户/社交媒体/复杂网络/表示学习/多模态融合
Key words
user identity linkage/user identification/entity user/social media/complex network/representation learning/multi-modal fusion
维普
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