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Interpreting and predicting social commerce intention based on knowledge graph analysis

Author

Listed:
  • Liu Yuan

    (Ministry of Education
    Shaanxi Normal University)

  • Zhao Huang

    (Ministry of Education
    Shaanxi Normal University)

  • Wei Zhao

    (Shaanxi Normal University)

  • Pavel Stakhiyevich

    (Shaanxi Normal University)

Abstract

There have been significant efforts to understand, describe, and predict the social commerce intention of users in the areas of social commerce and web data management. Based on recent developments in knowledge graph and inductive logic programming in artificial intelligence, in this paper, we propose a knowledge-graph-based social commerce intention analysis method. In particular, a knowledge base is constructed to represent the social commerce environment by integrating information related to social relationships, social commerce factors, and domain background knowledge. In this study, knowledge graphs are used to represent and visualize the entities and relationships related to social commerce, while inductive logic programming techniques are used to discover implicit information that can be used to interpret the information behaviors and intentions of the users. Evaluation tests confirmed the effectiveness of the proposed method. In addition, the feasibility of using knowledge graphs and knowledge-based data mining techniques in the social commerce environment is also confirmed.

Suggested Citation

  • Liu Yuan & Zhao Huang & Wei Zhao & Pavel Stakhiyevich, 2020. "Interpreting and predicting social commerce intention based on knowledge graph analysis," Electronic Commerce Research, Springer, vol. 20(1), pages 197-222, March.
    • Handle: RePEc:spr:elcore:v:20:y:2020:i:1:d:10.1007_s10660-019-09392-1
    DOI: 10.1007/s10660-019-09392-1
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    References listed on IDEAS

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