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User intent prediction search engine system based on query analysis and image recognition technologies

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Abstract

With the rapid development of the Internet and the World Wide Web, and the increasing amounts and variety of information on the Internet, people can now use search engines to obtain a diverse rich range of information. This paper proposes a user intent prediction search engine system (UIPSES) based on query history, using machine learning and deep learning image recognition technologies. Two different search methods are developed, based on a user keyword search and an upload image file search. The upload image file search uses deep learning image recognition technology to obtain multiple intent features for the image. Both the keyword and image searches use machine learning technology to extract multiple search intent feature information from the search logs, which is used as a basis for creating a user intent prediction for the keyword information search and the image file search. UIPSES provides highly correlated website index information between user browsing and predicted intent behaviour and uses machine learning to periodically train each user search process to update the user search intent recognition model to adapt to changes in the user intent, to improve the overall inference performance and analyse the accuracy of UIPSES, and to realise a search engine system with personalisation and a high-quality user experience. The UIPSES is a novel image search system that compares the relevance of search engine results for image and text information by using mean average precision with the well-known advanced web image search engines (Google, Bing, and Yandex). When the user uploads an image file for a search, the highest mean average precision value achieved by these three web image search engines was 2.28% for image information and text information feedback. In contrast, UIPSES can adapt to different conditions for single-object or multi-object images searches by obtaining multiple features from images and making inferences based on search logs, and therefore achieves high mean average precision values of 82.57 and 98.28%. UIPSES can also accurately find preset image and text information with higher relevance to allow users to search for images.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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  • 03 December 2022

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References

  1. Wachsmuth H, Potthast M, AlKhatib K, Ajjour Y, Puschmann J, Qu J, Dorsch J, Morari V, Bevendorff J, Stein B (2017) Building an Argument Search Engine for the Web, In: Workshop on Argument Mining, pp. 49–59

  2. Sharma D, Shukla R, Giri A, Kumar S (2019) A Brief Review on Search Engine Optimization, In: International Conference on Cloud Computing, Data Science & Engineering, pp. 687–691

  3. Rahman M, Abdullah N (2018) A personalized group-based recommendation approach for web search in E-learning. IEEE Access J 6:34166–34178

    Article  Google Scholar 

  4. Verma N, Malhotra D, Malhotra M, Singh J (2015) E-commerce website ranking using semantic web mining and neural computing. Proc Comput Sci 45:42–51

    Article  Google Scholar 

  5. Lewandowski D (2015) Evaluating the retrieval effectiveness of web search engines using a representative query sample. J Am Soc Inf Sci Technol 66(9):1763–1775

    Article  Google Scholar 

  6. Bosch A, Bogers T, Kunder M (2016) Estimating search engine index size variability: a 9-year longitudinal study. Scientometrics J 107:839–856

    Article  Google Scholar 

  7. Jo Y, Lee H, Cho A, Whang M (2021) Web behavior analysis in social life logging. J Supercomput 77:1301–1320

    Article  Google Scholar 

  8. Yang X, Mei T, Zhang Y, Liu J, Satoh S (2016) Web image search re-ranking with click-based similarity and typicality. IEEE Trans Image Process 25(10):4617–4630

    Article  MathSciNet  MATH  Google Scholar 

  9. Mezzoudj S, Behloul A, Seghir R, Saadna Y (2019) A parallel content-based image retrieval system using spark and tachyon frameworks. J King Saud Univ Comput Inf Sci 33(2):141–149

    Google Scholar 

  10. Han H, Li J, Jain A, Shan S, Chen X (2019) Tattoo image search at scale: joint detection and compact representation learning. IEEE Trans Pattern Anal Mach Intell 41(10):2333–2348

    Article  Google Scholar 

  11. Jain S, Dhar J (2017) Image Based Search Engine using Deep Learning, In: IEEE International Conference on Contemporary Computing, pp. 1–7

  12. Kim K, Kim J, Kim M, Sohn M (2021) User interest-based recommender system for image-sharing social media. World Wide Web J 24:1003–1025

    Article  Google Scholar 

  13. Tsai M, Tseng H (2021) Enhancing the identification accuracy of deep learning object detection using natural language processing. J Supercomput 77(6):6676–6691

    Article  Google Scholar 

  14. Babu R, Vanitha V, Anish K (2016) Content based image retrieval using color, texture, shape and active re-ranking method. Indian J Sci Technol 9(17):1–5

    Google Scholar 

  15. Raja R, Kumar S, Mahmood M (2020) Color object detection based image retrieval using ROI segmentation with multi-feature method. Wirel Personal Commun 112(1):169–192

    Article  Google Scholar 

  16. Mohamed O, Khalid E, Mohammed O, Brahim A (2019) Content Based Image Retrieval using Convolutional Neural Networks. In: Springer Advances Intelligent System Computing, pp. 463–476

  17. Diaz O, Perez M, Lascano J (2019) Literature review about intention mining in information systems. J Comput Inf Syst 61(2):1–10

    Google Scholar 

  18. Radford A, Kim J, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J, Krueger G, Sutskever I (2021) Learning Transferable Visual Models from Natural Language Supervision. In: International Conference on Machine Learning, pp. 1–47

  19. Szucs G, Papp D (2017) Content-based image retrieval for multiple objects search. Cybern Inf Technol 17(2):106–118

    Google Scholar 

  20. Zhang L, He Z, Yang Y, Wang L, Gao X (2020) Tasks integrated networks: joint detection and retrieval for image search. IEEE Trans Pattern Anal Mach Intell 44(1):1–18

    Google Scholar 

  21. Wang C, Pan H, Liu Y, Chen K, Qiu M, Zhou W, Huang J, Chen H. Lin W, Cai D (2021) Mell: Large-Scale Extensible User Intent Classification for Dialogue System with Meta Lifelong Learning, In: ACM Conference on Knowledge Discovery and Data Mining, pp. 3649–3659

  22. Google Search Engine (2020). https://www.google.com

  23. Microsoft Bing Search Engine (2021). https://www.bing.com

  24. Yandex Search Engine (2021). https://yandex.com

  25. Tang X, Liu K, Cui J, Wen F (2012) IntentSearch: capturing user intention for one-click internet image search. IEEE Trans Pattern Anal Mach Intell 34(7):1342–1353

    Article  Google Scholar 

  26. Dange B, Yadav S, Kshirsagar D (2020) Enhancing Image Retrieval and Re-Ranking Efficiency using Hybrid Approach, In: IEEE International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing, pp. 1–7

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Funding

This research was funded by National United University, Taiwan.

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Authors and Affiliations

  1. Department of Electronic Engineering, National United University, Miaoli, Taiwan

    Ming-Fong Tsai & Yi-Hong Wu

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  1. Ming-Fong Tsai
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  2. Yi-Hong Wu
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Contributions

M Tsai contributed to supervision; M Tsai and Y Wu were involved in writing—original draft; all authors have read and agreed to the published version of the manuscript.

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Correspondence to Ming-Fong Tsai.

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Tsai, MF., Wu, YH. User intent prediction search engine system based on query analysis and image recognition technologies. J Supercomput 79, 5327–5359 (2023). https://doi.org/10.1007/s11227-022-04874-w

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