Skip to main content
SpringerLink
Log in

Text Refinement Powered by Artificial Intelligence for Tourism

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Tourism is one of the leading industries across the globe. Tourists need to get information about the places to visit in their preferred destination. However, it is quite tedious to get individual information about these places. Thus, the proposed work aims in developing a system that analyses users likes and the preferred time period of the user to visit a particular location in the preferred place to form a trained data set and furnishes the results which showcase the path of a user can take to explore a specific city based on the trained data set. The proposed system adopts a haversine algorithm and traveling salesman problem in conjunction with a text refinement framework for extracting the information on recreational sites from media contents, social sites, and other web contents to form a rich corpus of data based on which the user is suggested appropriate places to visit. It is inferred from the experiment that the proposed approach has better time efficiency with changing user preferences, where the observed time is 1.33 ms with 16 user preferences.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Chaudhari, K., & Thakkar, A. (2019). A comprehensive survey on travel recommender systems. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-019-09363-7

    Article  Google Scholar 

  2. Rong, F. (2016). Design of tourism resource management based on artificial intelligence. In 2016 international conference on intelligent transportation, big data and smart city. ISBN (e): 978-1-5090-6061-0.

  3. Sengupta, L., Mariescu-Istodor, R., & Fränti, P. (2018). Planning your route: where to start? Computational Brain and Behavior. https://doi.org/10.1007/s42113-018-0018-0

    Article  Google Scholar 

  4. Smirnov, A., Kashevnik, A., Ponomarev, A., Shchekotov, M., & Kulakov, K. (2015). Application for e-tourism: Intelligent mobile tourist guide. In 2015 IIAI 4th international congress on advanced applied informatics. ISBN (e): 978-1-4799-958-3.

  5. Arifin, Z., Ibrahim, M. R., & Hatta, H. R. (2016). Nearest tourism site searching using haversine method. In Proceedings of 2016 3rd international conference on information technology, computer and electrical engineering (ICITACEE), Oct 19–21st, 2016, Semarang, Indonesia. ISBN (e): 978-1-5090-0890-2.

  6. Xu, H.-F., Gu, Y., Qi, J.-Z., He, J.-Y., & Yu, G. (2019). Diversifying top-k routes with spatial constraints. Journal of Computer Science and Technology, 34(4), 818–838. https://doi.org/10.1007/s11390-019-1944-6

    Article  MathSciNet  Google Scholar 

  7. Malik, S., & Kim, D. (2019). Optimal travel route recommendation mechanism based on neural networks and particle swarm optimization for efficient tourism using tourist vehicular data. Sustainability, 11(12), 3357. https://doi.org/10.3390/su11123357

    Article  Google Scholar 

  8. Kai, A., & Mingrui, X. (2012). A simple algorithm for solving traveling salesman problem. In 2012 second international conference on instrumentation and measurement, computer, communication and control. ISBN (e): 978-1-4673-5034-1.

  9. Wu, Y., Liang, Z., Liu, L. (2013). Design and implementation of tourism information system based on google maps API. In 2013 21st international conference on geoinformatics. ISBN (e): 978-1463-6228-3.

  10. Isoda, S., Hidaka, M., Matsuda, Y., Suwa, H., & Yasumoto, K. (2020). Timeliness-aware on-site planning method for tour navigation. Smart Cities, 3(4), 1383–1404. https://doi.org/10.3390/smartcities3040066

    Article  Google Scholar 

  11. Duffany, J. L. (2010). Artificial intelligence in GPS navigation systems. In 2010 2nd international conference on software technology and engineering (ICSTE). ISBN (e): 978-1-4244-8666-3.

  12. Dietz, L. W., Sen, A., Roy, R., & Wörndl, W. (2020). Mining trips from location-based social networks for clustering travelers and destinations. Information Technology and Tourism, 22, 131–166. https://doi.org/10.1007/s40558-020-00170-6

    Article  Google Scholar 

  13. Stock, K. (2018). Mining location from social media: A systematic review. Computers, Environment and Urban Systems, 71, 209–240. https://doi.org/10.1016/j.compenvurbsys.2018.05.007

    Article  Google Scholar 

  14. Winarno, E., Hadikurniawati, W., & Rosso, R. N. (2017). Location based service for presence system using haversine method. In 2017 international conference on innovative and creative information technology (ICITech). https://doi.org/10.1109/innocit.2017.8319153

  15. Pramanik, S., Haldar, R., Kumar, A., Pathak, S., & Mitra, B. (2019). Deep learning driven venue recommender for event-based social networks. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/tkde.2019.2915523

    Article  Google Scholar 

  16. Wen, Y.-T., Yeo, J., Peng, W.-C., & Hwang, S.-W. (2017). Efficient keyword-aware representative travel route recommendation. IEEE Transaction on Knowledge and Data Engineering, 29(8), 1041–4347

    Article  Google Scholar 

  17. Renjith, S., Sreekumar, A., & Jathavedan, M. (2019). An extensive study on the evolution of context-aware personalized travel recommender systems. Information Processing and Management. https://doi.org/10.1016/j.ipm.2019.102078

    Article  Google Scholar 

  18. Mrazovic, P., Larribo-Pey, J. L., & Matskin, M. (2017). Improving mobility in smart cities with intelligent tourist trip planning. In 2017 IEEE 41st annual computer software and application conference. ISBN (e): 978-5386-0367-3.

  19. Huang, F., Xu, J., & Weng, J. (2020). Multi-task travel route planning with a flexible deep learning framework. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/tits.2020.2987645

    Article  Google Scholar 

  20. Sylejmani, K., Muhaxhiri, A., Dika, A., & Ahmedi, L. (2014). Solving tourist trip planning problem via a simulated annealing algorithm. MIPRO 2014, Opatija, Croatia. ISBN (e): 978-953-233-0-9.

  21. Hau, W., Wang, Z., Wang, H., Zheng, K., & Zhou, X. (2017). Understandshort texts by harvesting and analyzing semantic knowledge. IEEE Transactions on Knowledge and Data Engineering, 29(3), 499–512. https://doi.org/10.1109/TKDE.2016.2571687

    Article  Google Scholar 

  22. Spasic, I. (2018). Acronyms as an integral part of multi-word term recognition: A token of appreciation. IEEE Access, 6, 8351–8363. https://doi.org/10.1109/ACCESS.2018.2807122,February

    Article  Google Scholar 

  23. Goulartea, F. B., Nassar, S. M., Filetoa, R., & Saggion, H. (2019). A text summarization method based on fuzzy rules and applicable to automated assessment. Expert Systems with Applications.

  24. Mohamed, M., & Oussalah, M. (2019). SRL-ESA-TextSum: A text summarization approach based on semantic role labeling and explicit semantic analysis. Information Processing and Management.

Download references

Author information

Authors and Affiliations

  1. Department of Information Science and Engineering, Sahyadri College of Engineering and Management, Mangaluru, Karnataka, India

    Prakhyath Rai & Shamanth Rai

Authors
  1. Prakhyath Rai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shamanth Rai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shamanth Rai.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rai, P., Rai, S. Text Refinement Powered by Artificial Intelligence for Tourism. Wireless Pers Commun 120, 1193–1205 (2021). https://doi.org/10.1007/s11277-021-08510-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08510-3

Keywords

Search

Navigation