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Domain Knowledge-Based Link Prediction in Customer-Product Bipartite Graph for Product Recommendation

Author

Listed:
  • Lingling Zhang

    (School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China†Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing, 100190, China)

  • Jing Li

    (School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China*School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China)

  • Qiuliu Zhang

    (School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China)

  • Fan Meng

    (School of Management Science and Engineering, Central University of Finance and Economics, Beijing, 102206, China)

  • Weili Teng

    (Nottingham Business School, Nottingham Trent University, City Campus, Nottingham NG1 4BU, UK)

Abstract

In this paper, we propose domain knowledge-based link prediction algorithm in customer-product bipartite network to improve effectiveness of product recommendation in retail. The domain knowledge is classified into product domain knowledge and time context knowledge, which play an important part in link prediction. We take both of them into consideration in recommendation and form a unified domain knowledge-based link prediction framework. We capture product semantic similarity by ontology-based analysis and time attenuation factor from time context knowledge, then incorporate them into network topological similarity to form a new linkage measure. To evaluate the algorithm, we use a real retail transaction dataset from Food Mart. Experimental results demonstrate that the usage of domain knowledge in link prediction achieved significantly better performance.

Suggested Citation

  • Lingling Zhang & Jing Li & Qiuliu Zhang & Fan Meng & Weili Teng, 2019. "Domain Knowledge-Based Link Prediction in Customer-Product Bipartite Graph for Product Recommendation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 311-338, January.
    • Handle: RePEc:wsi:ijitdm:v:18:y:2019:i:01:n:s0219622018410031
    DOI: 10.1142/S0219622018410031
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    References listed on IDEAS

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    1. Shrutika Mishra, 2018. "Financial management and forecasting using business intelligence and big data analytic tools," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 5(02), pages 1-16, June.
    2. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
    3. Aaron Clauset & Cristopher Moore & M. E. J. Newman, 2008. "Hierarchical structure and the prediction of missing links in networks," Nature, Nature, vol. 453(7191), pages 98-101, May.
    4. Liu, Ji & Deng, Guishi, 2009. "Link prediction in a user–object network based on time-weighted resource allocation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3643-3650.
    5. Daning Hu & Gerhard Schwabe & Xiao Li, 2015. "Systemic risk management and investment analysis with financial network analytics: research opportunities and challenges," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-9, December.
    6. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    7. Naoki Shibata & Yuya Kajikawa & Ichiro Sakata, 2012. "Link prediction in citation networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(1), pages 78-85, January.
    8. Kou, Gang & Ergu, Daji & Shang, Jennifer, 2014. "Enhancing data consistency in decision matrix: Adapting Hadamard model to mitigate judgment contradiction," European Journal of Operational Research, Elsevier, vol. 236(1), pages 261-271.
    9. Zan Huang & Wingyan Chung & Hsinchun Chen, 2004. "A graph model for E‐commerce recommender systems," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 55(3), pages 259-274, February.
    10. Zan Huang & Daniel D. Zeng & Hsinchun Chen, 2007. "Analyzing Consumer-Product Graphs: Empirical Findings and Applications in Recommender Systems," Management Science, INFORMS, vol. 53(7), pages 1146-1164, July.
    11. Naoki Shibata & Yuya Kajikawa & Ichiro Sakata, 2012. "Link prediction in citation networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(1), pages 78-85, January.
    12. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
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