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Research on Bidding Case Recommendation Algorithm Considering Bidding Features

In: Liss 2020

Author

Listed:
  • Wenting Liang

    (University of Science and Technology Beijing)

  • Hao Liu

    (University of Science and Technology Beijing)

  • Yaoyu Hu

    (University of Science and Technology Beijing)

Abstract

In this paper a case recommendation matrix is proposed based on the content unit that can be used for reference in the bidding case. The case feature matrix is obtained based on the content recommendation algorithm, and the similarity between the new bidding case and the historical bidding case is calculated by using the cosine similarity algorithm. Based on the sequence of similarity calculation results, the similarity judgment threshold of the cases is determined, and historical bidding cases that are similar to the new bidding cases are obtained to form a set of recommended cases. In order to verify the feasibility and effectiveness of the proposed algorithm, an experimental analysis was performed using the algorithm. A set of cases that meet the requirements can be recommended by the recommendation algorithm proposed in this paper, which makes related business personnel more convenient in actual.

Suggested Citation

  • Wenting Liang & Hao Liu & Yaoyu Hu, 2021. "Research on Bidding Case Recommendation Algorithm Considering Bidding Features," Springer Books, in: Shifeng Liu & Gábor Bohács & Xianliang Shi & Xiaopu Shang & Anqiang Huang (ed.), Liss 2020, pages 665-676, Springer.
    • Handle: RePEc:spr:sprchp:978-981-33-4359-7_46
    DOI: 10.1007/978-981-33-4359-7_46
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