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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

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Social network supported process recommender system.

Yanming Ye1, Jianwei Yin2, Yueshen Xu2

  • 1College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China ; College of Information Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.

Thescientificworldjournal
|March 28, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel process recommendation system that incorporates social features alongside structural analysis for business process modeling. It demonstrates improved recommendations by leveraging process similarity networks.

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Area of Science:

  • Computer Science
  • Information Systems
  • Social Network Analysis

Background:

  • Process recommendation technologies aid intelligent business process modeling.
  • Existing methods often overlook crucial social features, relying solely on process structure.
  • Business process modeling is frequently complex and comprehensive.

Purpose of the Study:

  • To explore the integration of social network research into process recommendation systems.
  • To develop a social network system for processes based on feature similarities.
  • To enhance the accuracy and comprehensiveness of process recommendations.

Main Methods:

  • Building a social network of processes using feature similarity.
  • Developing three distinct metrics for measuring process matching degrees.
  • Implementing and discussing the social network system for process recommendation.

Main Results:

  • Experimental evaluations indicate the feasibility and effectiveness of the proposed approach.
  • The system successfully incorporates social features for improved process recommendations.
  • The developed matching degree measurements provide quantifiable comparisons between processes.

Conclusions:

  • Social network research technologies offer a viable enhancement for process recommendation.
  • Integrating social features alongside structural analysis leads to more robust process modeling.
  • Future work should further refine social network integration and explore advanced recommendation algorithms.