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Your search keyword '"Ren, Lingfei"' showing total 31 results
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31 results on '"Ren, Lingfei"'

9. A Bi-directional Category-Aware Multi-task Learning Framework for Missing Check-in POI Identification

Author

Wu, Junhang, Hu, Ruimin, Li, Dengshi, Ren, Lingfei, Hu, Wenyi, Zang, Yilong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Troya, Javier, editor, Medjahed, Brahim, editor, Piattini, Mario, editor, Yao, Lina, editor, Fernández, Pablo, editor, and Ruiz-Cortés, Antonio, editor

Published
2022
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10. IDGL: An Imbalanced Disassortative Graph Learning Framework for Fraud Detection

Author

Wu, Junhang, Hu, Ruimin, Li, Dengshi, Ren, Lingfei, Hu, Wenyi, Zang, Yilong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Troya, Javier, editor, Medjahed, Brahim, editor, Piattini, Mario, editor, Yao, Lina, editor, Fernández, Pablo, editor, and Ruiz-Cortés, Antonio, editor

Published
2022
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26. Where Have You Gone: Category-aware Multigraph Embedding for Missing Point-of-Interest Identification.

Author

Wu, Junhang, Hu, Ruimin, Li, Dengshi, Xiao, Yilin, Ren, Lingfei, and Hu, Wenyi

Subjects
MULTIGRAPH, SOCIAL networks, TASK performance, DATA quality, SHARED workspaces
Abstract

The prevalence of location-based social networks (LBSNs) provides an opportunity for human mobility behavior understanding and prediction. However, data quality issues (e.g., historical check-in POI missing, data sparsity) always limit the effectiveness of existing LBSN-oriented studies, e.g., next Point-of-Interest (POI) recommendation or prediction. In contrast to previous efforts in the above study, we focus on identifying missing POIs that the user has visited at a past specific time and develop a category-aware multigraph embedding (CAME) model. Specifically, CAME jointly captures temporal cyclic effect, user preference, and sequential transition pattern in a unified way by embedding five relational information graphs into a shared dimensional space from both POI- and category-instance levels. The proposed model also incorporates region-level spatial proximity to explore the geographical influence and derives the ranking score list of candidates for missing POI identification. Extensive experiments against state-of-the-art methods are conducted on two real datasets, and the experimental results show its superiority over other competitors. Significantly, the proposed model can be naturally extended to next POI recommendation and prediction tasks with competitive performances. [ABSTRACT FROM AUTHOR]

Published
2023
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