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This paper presents a novel unsupervised Gradient Semantic Model for User Alignment (GSMUA) for the purpose of identifying common user identities across social networks. GSMUA categorises user profile information into weak, sub, and strong gradients based on the semantic intensity of attributes. Different gradient semantic levels direct attention to literal features, semantic features, or a combination of both during feature extraction, thereby achieving a full semantic representation of user attributes. In the case of strongly semantic long texts, GSMUA employs Named Entity Recognition (ENR) technology in order to enhance the inefficient handling of such texts. Furthermore, GSMUA compensates for missing user profile attributes by utilising profile information from user neighbours, thereby reducing the negative impact of missing user profile attributes on model performance. Extensive experiments conducted on four pairs of real datasets demonstrate the superiority of our approach. In comparison to the most effective previously developed unsupervised methods, GSMUA demonstrates improvements in hit-precision ranging from 5.32 to 12.17%. When compared to supervised methods, the improvements range from 0.71 to 11.79%.<\/jats:p>","DOI":"10.1007\/s40747-024-01626-6","type":"journal-article","created":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T03:49:39Z","timestamp":1731383379000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Unveiling user identity across social media: a novel unsupervised gradient semantic model for accurate and efficient user alignment"],"prefix":"10.1007","volume":"11","author":[{"given":"Yongqiang","family":"Peng","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8201-9631","authenticated-orcid":false,"given":"Xiaoliang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Duoqian","family":"Miao","sequence":"additional","affiliation":[]},{"given":"Xiaolin","family":"Qin","sequence":"additional","affiliation":[]},{"given":"Xu","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,12]]},"reference":[{"key":"1626_CR1","doi-asserted-by":"publisher","unstructured":"Zhao C, Zhao H, He M, Zhang J, Fan J (2023) Cross-domain recommendation via user interest alignment. 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