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Interpreting and predicting social commerce intention based on knowledge graph analysis

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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.

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Acknowledgements

This study was supported by research grants funded by the National Natural Science Foundation of China (Grant No. 61771297), and the “Fundamental Research Funds for the Central Universities” (GK201803062, GK201802013).

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Authors and Affiliations

  1. Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an, People’s Republic of China

    Liu Yuan & Zhao Huang

  2. School of Computer Science, Shaanxi Normal University, 620 West Chang’an Street, Xi’an, 710119, People’s Republic of China

    Liu Yuan, Zhao Huang, Wei Zhao & Pavel Stakhiyevich

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  1. Liu Yuan
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Correspondence to Zhao Huang.

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Yuan, L., Huang, Z., Zhao, W. et al. Interpreting and predicting social commerce intention based on knowledge graph analysis. Electron Commer Res 20, 197–222 (2020). https://doi.org/10.1007/s10660-019-09392-1

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