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Analysis Method of App Software User Experience Based on Multisource Information Fusion

Author

Listed:
  • Yongquan Chen

    (Kunming University of Science and Technology, China)

  • Ying Jiang

    (Kunming University of Science and Technology, China)

  • Haiyi Liu

    (Kunming University of Science and Technology, China)

Abstract

With the rapid development and popularization of intelligent terminals, app software has also developed rapidly. The research and practical value of mining user experience (UX) of app software form interaction information are becoming increasingly prominent. The interactive information of app software is multisource homogeneous and heterogeneous. In order to obtain more accurate and more comprehensive app software UX results, the fused multisource information should be analyzed. In this paper, the app software UX analysis method based on multisource information fusion is proposed. First, feature engineering is carried out to extract the features. Then, the feature combination tree is constructed after feature correlation mining. Finally, the multisource app software interactive data are fused, and the result is further analyzed to obtain the information of app software UX. The experiments clearly show that the method can effectively fuse multisource app software interaction data and help to comprehensively mine the app software UX embodied in the data.

Suggested Citation

  • Yongquan Chen & Ying Jiang & Haiyi Liu, 2023. "Analysis Method of App Software User Experience Based on Multisource Information Fusion," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 19(1), pages 1-22, January.
    • Handle: RePEc:igg:jswis0:v:19:y:2023:i:1:p:1-22
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    References listed on IDEAS

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    1. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    2. Shutian Ma & Chengzhi Zhang & Xiaozhong Liu, 2020. "A review of citation recommendation: from textual content to enriched context," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1445-1472, March.
    3. Xi Chen & Huan-jing Zhao & Shu Zhao & Jie Chen & Yan-ping Zhang, 2019. "Citation recommendation based on citation tendency," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 937-956, November.
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