RESEARCH PAPERS

Personalized Academic Retrieval Research by Integrating Word Embedding Model and Frequent Sequence Mining Techniques

  • Men Yuying ,
  • Jiao Xuan ,
  • Wei Qingfeng
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  • 1 Hubei Provincial Academy of Science and Technology Information, Wuhan 430071;
    2 Central China Normal University, Wuhan 430079;
    3 Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097
Men Yuying, Director, Researcher, PhD, Master Supervisor, E-mail: 24495883@qq.com; Jiao Xuan, Master’s Candidate; Wei Qingfeng, Deputy Director, Researcher, Master’s Degree.

Received date: 2024-09-03

  Revised date: 2025-02-11

  Online published: 2025-09-12

Supported by

This work is supported by project of Hubei Provincial Soft Science Program, titled “Research on the Construction Path of Hubei Provincial Science and Technology Resource Integration Center” (Grant No. 2024EDA002).

Abstract

[Purpose/Significance] A new personalized literature retrieval method is proposed to solve the problems of retrieval efficiency and accuracy caused by the massive literature data stored in the integrated platform. [Method/Process] The method was based on the web user access log of Hubei Science and Technology information sharing service platform, combined the word embedding model (GloVe) and frequent sequence mining technology. By mining high-frequency sequences of user click behaviors to capture user interest, word embedding model was used to generate context-sensitive literature noun vectors, and then similarity was calculated to recommend the Top 5 most relevant literatures. [Result/Conclusion] This method can effectively improve the accuracy rate, recall rate, and F1 value of retrieval, providing a new solution for personalized academic paper retrieval on the integrated platform. Meanwhile, this study integrates and applies the GloVe model and frequent sequence mining technology to the field of academic paper retrieval for the first time, expanding the collaborative application scenarios of the two types of technologies.

Cite this article

Men Yuying , Jiao Xuan , Wei Qingfeng . Personalized Academic Retrieval Research by Integrating Word Embedding Model and Frequent Sequence Mining Techniques[J]. Library and Information Service, 2025 , 69(19) : 124 -133 . DOI: 10.13266/j.issn.0252-3116.2025.19.011

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