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Research on User Behavior Based on Higher-Order Dependency Network.

Liwei Qian1, Yajie Dou1, Chang Gong1

  • 1College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.

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|August 26, 2023
PubMed
Summary
This summary is machine-generated.

Analyzing daily behavior using higher-order dependency networks (HONs) improves requirement mining. This method enhances accuracy in identifying key user behaviors and understanding community structures, benefiting personalized recommendations and product development.

Keywords:
behavior sequence analysiscommunity detectionhigher-order dependency networks (HONs)random walkvital node identification

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Area of Science:

  • Computer Science
  • Data Mining
  • Network Analysis

Background:

  • Internet of Things (IoT) data enables analysis of daily life behaviors to identify user needs.
  • Current network methods, like first-order networks (FONs), overlook crucial high-order dependencies in behavior sequences.

Purpose of the Study:

  • To introduce a higher-order dependency network (HON) model for more accurate user behavior analysis.
  • To improve the mining of user requirements by considering complex behavioral relationships.

Main Methods:

  • Behavior sequences from video detection were used to extract higher-order dependency rules and construct an HON.
  • The HON was applied to the RandomWalk algorithm for vital node identification and community detection.

Main Results:

  • HONs significantly improved the accuracy of the RandomWalk algorithm compared to FONs.
  • Identification of vital nodes was enhanced, and it was observed that nodes can belong to multiple communities.
  • The study demonstrated improved performance in user behavior analysis.

Conclusions:

  • Higher-order dependency networks offer a more accurate approach to analyzing user behavior and mining requirements.
  • Findings support enhanced personalized recommendations, product improvements, and increased commercial profits.
  • The research highlights the importance of considering complex dependencies in network analysis for behavioral data.