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Intelligent Approval of Access Control Flow in Office Automation Systems via Relational Modeling

arXiv 2604.11040 ↗First posted 13 Apr 2026cs.AIcited by 0

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Abstract (arXiv)

Office automation (OA) systems play a crucial role in enterprise operations and management, with access control flow approval (ACFA) being a key component that manages the accessibility of various resources. However, traditional ACFA requires approval from the person in charge at each step, which consumes a significant amount of manpower and time. Its intelligence is a crucial issue that needs to be addressed urgently by all companies. In this paper, we propose a novel relational modeling-driven intelligent approval (RMIA) framework to automate ACFA. Specifically, our RMIA consists of two core modules: (1) The binary relation modeling module aims to characterize the coupling relation between applicants and approvers and provide reliable basic information for ACFA decision-making from a coarse-grained perspective. (2) The ternary relation modeling module utilizes specific resource information as its core, characterizing the complex relations between applicants, resources, and approvers, and thus provides fine-grained gain information for informed decision-making. Then, our RMIA effectively fuses these two kinds of information to form the final decision. Finally, extensive experiments are conducted on two product datasets and an online A/B test to verify the effectiveness of RMIA.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 32 references that are in the map. First posted on arXiv 13 Apr 2026.

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References · 6

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 673
  4. 2020DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systemscited by 326
  5. 2023FinalMLP: An Enhanced Two-Stream MLP Model for CTR Predictioncited by 37
  6. 2023EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Predictioncited by 26

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