Research on Bidding Case Recommendation Algorithm Considering Bidding Features

Wenting Liang (), Hao Liu () and Yaoyu Hu ()
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Wenting Liang: University of Science and Technology Beijing
Hao Liu: University of Science and Technology Beijing
Yaoyu Hu: University of Science and Technology Beijing

A chapter in LISS 2020, 2021, pp 665-676 from Springer

Abstract: 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.

Keywords: Recommendation algorithm; Case characteristics; Bidding (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-33-4359-7_46

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DOI: 10.1007/978-981-33-4359-7_46

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