Skip to main content

Content Recommendation of Tender Documents Based on Qualitative Characteristics

  • Conference paper
  • First Online:
LISS 2020

Abstract

Aiming at content recommendation of tender documents, this paper puts forward the case reuse and case modification algorithm of tender documents. First, according to the usage of clauses in tender cases, this paper uses non-interference sequence index to cluster similar tender cases and similar clauses, then based on which the reference samples and content modules of the tender documents were constructed. Finally, recommended value of reference samples and difference degrees between content modules were used respectively to realize content recommendation. This algorithm ensures the scientificity of the tender documents’ preparation and the accuracy of the recommended content, and greatly improves the efficiency while reducing the scope of the recommended content.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. S. Wang, Factor analysis of the impact of the preparation of tender documents on the project cost. Architect. Eng. Technol. Des. 7, 248+254 (2018)

    Google Scholar 

  2. H. Ying, Problems and Countermeasures in the compilation of project tender document. Build. Mater. Decoration 8 (2019)

    Google Scholar 

  3. J. Ling, Preparation of tender documents. Sci. Wealth 12, 122 (2019)

    Google Scholar 

  4. R. Wang, Design and development of electronic tender documents compilation system—exploration of Baohua Tender’s Second Generation Electronic Tender Platform. China Tender 8, 22–25 (2013)

    Google Scholar 

  5. S. Goswami, P. Bharwaj, S. Kapoor, Naïve Bayes classification of DRDO tender documents, in 2014 International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, pp. 593–597 (2014)

    Google Scholar 

  6. J. Tang, Concepts and methods of machine writing tender documents. Tender Procurement Manage. 9, 43–46 (2019)

    Google Scholar 

  7. S. Wu, Q. Wang, M. Jiang et al., Clustering algorithm of categorical data in consideration of sorting by weight. J. Univ. Sci. Technol. Beijing 35(38), 1093–1098 (2013)

    Google Scholar 

  8. L. Xu, X. Li, M. Yu, Bayesian network modeling method based on case reasoning for emergency decision-making. J. Shanghai Normal Univ. Nat. Sci. 42, 237–243 (2013)

    Google Scholar 

  9. S. Laryea, Quality of tender documents: case studies from the UK. Constr. Manag. Econ. 29, 275–286 (2011)

    Article  Google Scholar 

  10. J. Li, S.X. Pan, L. Huang, X. Zhu, A machine learning based method for customer behavior prediction. Tehnicki Vjesnik-Technical Gazette 26(6), 1670–1676 (2019). https://doi.org/10.17559/TV-20190603165825

    Article  Google Scholar 

  11. L.L. Qin, N.W. Yu, D.H. Zhao, Applying the convolutional neural network deep learning technology to behavioural recognition in intelligent video. Tehnicki Vjesnik-Technical Gazette 25(2), 528–535 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Guiying Wei .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhou, T., Wei, G., Wang, A. (2021). Content Recommendation of Tender Documents Based on Qualitative Characteristics. In: Liu, S., Bohács, G., Shi, X., Shang, X., Huang, A. (eds) LISS 2020. Springer, Singapore. https://doi.org/10.1007/978-981-33-4359-7_22

Download citation

Publish with us

Policies and ethics