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Prohibited Item Detection via Risk Graph Structure Learning

Published: 25 April 2022 Publication History

Abstract

Prohibited item detection is an important problem in e-commerce, where the goal is to detect illegal items online for evading risks and stemming crimes. Traditional solutions usually mine evidence from individual instances, while current efforts try employing advanced Graph Neural Networks (GNN) to utilize multiple risk-relevant structures of items. However, it still remains two essential challenges, including weak structure and weak supervision. This work proposes the Risk Graph Structure Learning model (RGSL) for prohibited item detection. RGSL first introduces structure learning into large-scale risk graphs, to reduce noisy connections and add similar pairs. It then designs the pairwise training mechanism, which transforms the detection process as a metric learning from candidates to their similar prohibited items. Furthermore, RGSL generates risk-aware item representations and searches risk-relevant pairs for structure learning iteratively. We test RGSL on three real-world scenarios, and the improvements to baselines are up to 21.91% in AP and 18.28% in MAX-F1. Meanwhile, RGSL has been deployed on an e-commerce platform, and the improvements to traditional solutions are up to 23.59% in ACC@1000 and 6.52% in ACC@10000.

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Cited By

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  • (2024)GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud DetectionIEEE Transactions on Big Data10.1109/TBDATA.2024.335297810:4(528-542)Online publication date: Aug-2024
  • (2024)Improved deep neural network (EnhanceNet) for real-time detection of some publicly prohibited itemsNetwork: Computation in Neural Systems10.1080/0954898X.2024.2398531(1-28)Online publication date: 11-Sep-2024
  • (2024)DAHGN: Degree-Aware Heterogeneous Graph Neural NetworkKnowledge-Based Systems10.1016/j.knosys.2023.111355285(111355)Online publication date: Feb-2024
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      cover image ACM Conferences
      WWW '22: Proceedings of the ACM Web Conference 2022
      April 2022
      3764 pages
      ISBN:9781450390965
      DOI:10.1145/3485447
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      Published: 25 April 2022

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      Author Tags

      1. graph structure learning
      2. pairwise labeling
      3. prohibited item detection
      4. risk graph

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      April 25 - 29, 2022
      Virtual Event, Lyon, France

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      Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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      View all
      • (2024)GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud DetectionIEEE Transactions on Big Data10.1109/TBDATA.2024.335297810:4(528-542)Online publication date: Aug-2024
      • (2024)Improved deep neural network (EnhanceNet) for real-time detection of some publicly prohibited itemsNetwork: Computation in Neural Systems10.1080/0954898X.2024.2398531(1-28)Online publication date: 11-Sep-2024
      • (2024)DAHGN: Degree-Aware Heterogeneous Graph Neural NetworkKnowledge-Based Systems10.1016/j.knosys.2023.111355285(111355)Online publication date: Feb-2024
      • (2023)Datasets and Interfaces for Benchmarking Heterogeneous Graph Neural NetworksProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615117(5346-5350)Online publication date: 21-Oct-2023
      • (2023)Knowledge Based Prohibited Item Detection on Heterogeneous Risk GraphsProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599852(5260-5269)Online publication date: 4-Aug-2023
      • (2023)EGNN: Graph structure learning based on evolutionary computation helps more in graph neural networksApplied Soft Computing10.1016/j.asoc.2023.110040135(110040)Online publication date: Mar-2023

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