[go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Enhancing scenic recommendation and tour route personalization in tourism using UGC text mining

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Tourism is vital to national economic growth and fulfilling individuals’ spiritual pursuits. However, traditional scenic recommendation algorithms must improve accuracy, susceprecommended route personalizationtion of recommended tour routes, and suboptimal tour value. This study presents a UGC text mining-based travel recommendation method to address these problems. To begin with, the proposed method utilizes text mining techniques to enrich scenic tag data and introduces the recommendation algorithm based on fuzzy label-matching user-item characteristics (FLMC). Additionally, we incorporate scenic attributes and travel time between scenics to identify four key attributes that impact scenic selection in tour routes. Users’ subjective preferences for these attributes are calculated, and objective weights derived from information entropy are used to determine combination weights for scenic selection. This ensures a more personalized and optimized tour route. Lastly, the proposed method features a tour route ranking method that selects the highest-scoring route as the recommended option. This approach enhances the overall quality of the recommended tour routes. Through experimentation with 240,194 reviews of 369 scenics in Beijing, in the attraction recommendation scenario, the proposed FLMC algorithm achieves up to 75.40% accuracy, which is higher than the other three compared algorithms (46.48%, 35.90%, and 32.98%). In the travel route recommendation scenario, the proposed method has a route gain value of up to 1.57 and a user experience value of recommended route personalizationd compared to the other three compared algorithms (1.03, 0.9; 0.95, 0.53; 1.12, 0.85). These results highlight the significant potential of the proposed method in improving the personalization of travel recommendations and enhancing tourist experiences.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 7
Algorithm 1
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Algorithm 2
Fig. 13
Fig. 14

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

The data used to support the findings of this study are included in the article.

References

  1. Buhalis D (2020) Technology in tourism-from information communication technologies to etourism and smart tourism towards ambient intelligence tourism: a perspective article. Tourism Review. 75(1):267–272

    Google Scholar 

  2. Tussyadiah I (2020) A review of research into automation in tourism: Launching the annals of tourism research curated collection on artificial intelligence and robotics in tourism. Ann Tour Res 81:102883

    Google Scholar 

  3. Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) A survey on algorithmic approaches for solving tourist trip design problems. Journal of Heuristics. 20:291–328

    Google Scholar 

  4. Souffriau W, Vansteenwegen P, Vertommen J, Berghe GV, Oudheusden DV (2008) A personalized tourist trip design algorithm for mobile tourist guides. Appl Artif Intell 22(10):964–985

  5. Samara D, Magnisalis I, Peristeras V (2020) Artificial intelligence and big data in tourism: a systematic literature review. J Hosp Tour Technol 11(2):343–367

    Google Scholar 

  6. Vansteenwegen P, Van Oudheusden D (2007) The mobile tourist guide: an or opportunity. OR insight. 20:21–27

    Google Scholar 

  7. Vansteenwegen P, Souffriau W, Berghe GV, Van Oudheusden D (2011) The city trip planner: an expert system for tourists. Expert Syst Appl 38(6):6540–6546

    Google Scholar 

  8. Archetti C, Hertz A, Speranza MG (2007) Metaheuristics for the team orienteering problem. Journal of heuristics. 13:49–76

    Google Scholar 

  9. Wu L, He X, Wang X, Zhang K, Wang M (2022) A survey on accuracy oriented neural recommendation: From collaborative filtering to information-rich recommendation. IEEE Transactions on Knowledge and Data Engineering

  10. Chen L, Cao J, Wang Y, Liang W, Zhu G (2022) Multi-view graph attention network for travel recommendation. Expert Syst Appl 191:116234

    Google Scholar 

  11. Zhu G, Wang Y, Cao J, Bu Z, Yang S, Liang W, Liu J (2021) Neural attentive travel package recommendation via exploiting long-term and short-term behaviors. Knowl-Based Syst 211:106511

    Google Scholar 

  12. Batmaz Z, Yurekli A, Bilge A, Kaleli C (2019) A review on deep learning for recommender systems: challenges and remedies. Artif Intell Rev 52:1–37

    Google Scholar 

  13. Zheng J, Wang S, Li D, Zhang B (2019) Personalized recommendation based on hierarchical interest overlapping community. Inf Sci 479:55–75

    Google Scholar 

  14. Liao M, Sundar SS (2022) When e-commerce personalization systems show and tell: Investigating the relative persuasive appeal of content-based versus collaborative filtering. J Advert 51(2):256–267

    Google Scholar 

  15. Afoudi Y, Lazaar M, Al Achhab M (2021) Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network. Simul Model Pract Theory 113:102375

    Google Scholar 

  16. Pérez-Almaguer Y, Yera R, Alzahrani AA, Martínez L (2021) Content-based group recommender systems: A general taxonomy and further improvements. Expert Syst Appl 184:115444

    Google Scholar 

  17. Van Dat N, Van Toan P, Thanh TM (2022) Solving distribution problems in content-based recommendation system with gaussian mixture model. Appl Intell 52(2):1602–1614

  18. Chang JL, Li H, Bi JW (2022) Personalized travel recommendation: a hybrid method with collaborative filtering and social network analysis. Curr Issue Tour 25(14):2338–2356

    Google Scholar 

  19. Kuo R, Li SS (2023) Applying particle swarm optimization algorithm-based collaborative filtering recommender system considering rating and review. Applied Soft Computing. 110038

  20. Sharma R, Gopalani D, Meena Y (2023) An anatomization of research paper recommender system: Overview, approaches and challenges. Eng Appl Artif Intell 118:105641

    Google Scholar 

  21. Zhou X, Tian J, Peng J, Su M (2021) A smart tourism recommendation algorithm based on cellular geospatial clustering and multivariate weighted collaborative filtering. ISPRS Int J Geo Inf 10(9):628

    Google Scholar 

  22. Morise H, Atarashi K, Oyama S, Kurihara M (2022) Neural collaborative filtering with multicriteria evaluation data. Appl Soft Comput 119:108548

    Google Scholar 

  23. Chen L, Cao J, Zhu G, Wang Y, Liang W (2021) A multi-task learning approach for improving travel recommendation with keywords generation. Knowledge Based Systems. 233:107521

    Google Scholar 

  24. Cheng X (2021) A travel route recommendation algorithm based on interest theme and distance matching. EURASIP Journal on Advances in Signal Processing. 2021(1):1–10

    Google Scholar 

  25. Xu M, Liu H (2021) A flexible deep learning-aware framework for travel time prediction considering traffic event. Eng Appl Artif Intell 106:104491

    Google Scholar 

  26. Liu Y, Pei A, Wang F, Yang Y, Zhang X, Wang H, Dai H, Qi L, Ma R (2021) An attention-based category-aware gru model for the next poi recommendation. Int J Intell Syst 36(7):3174–3189

    Google Scholar 

  27. Bhatti UA, Tang H, Wu G, Marjan S, Hussain A (2023) Deep learning with graph convolutional networks: An overview and latest applications in computational intelligence. Int J Intell Syst 2023:1–28

    Google Scholar 

  28. Wang L (2022) Travelling route recommendation method based on graph neural network for improving travel experience. Journal of Circuits, Systems and Computers. 2350102

  29. Chen L, Cao J, Tao H, Wu J (2023) Trip reinforcement recommendation with graph-based representation learning. ACM Trans Knowl Discov Data 17(4):1–20

    Google Scholar 

  30. Gao Q, Wang W, Huang L, Yang X, Li T, Fujita H (2023) Dual-grained human mobility learning for location-aware trip recommendation with spatial–temporal graph knowledge fusion. Information Fusion. 92:46–63

  31. Wang D, Zhu J, Yin Y, Ignatius J, Wei X, Kumar A (2023) Dynamic travel time prediction with spatiotemporal features: using a gnn-based deep learning method. Annals of Operations Research. 1–21

  32. Park ST, Liu C (2022) A study on topic models using lda and word2vec in travel route recommendation: focus on convergence travel and tours reviews. Personal and Ubiquitous Computing. 1–17

  33. Yingju Z (2020) Research on flow patterns of tourists in scenic spots based on data mining. In: E3S Web of Conferences. vol. 214, p. 01014. EDP Sciences

  34. Lee HJ, Lee WS, Choi IH, Lee CK (2020) Sequence-based travel route recommendation systems using deep learning-a case of jeju island. Smart Media Journal. 9(1):45–50

    Google Scholar 

  35. Singh A, Babu S (2020) Travel route recommendation system using user keyword search. Int. J Recent Technol. Eng. 8(6):2052–2056

    Google Scholar 

  36. Lan F, Huang Q, Zeng L, Guan X, Xing D, Cheng Z (2021) Tourism experience and construction of personalized smart tourism program under tourist psychology. Front Psychol 12:691183

    Google Scholar 

  37. Du S, Zhang H, Xu H, Yang J, Tu O (2019) To make the travel healthier: a new tourism personalized route recommendation algorithm. J Ambient Intell Humaniz Comput 10:3551–3562

    Google Scholar 

  38. Hamid RA, Albahri AS, Alwan JK, Al-Qaysi Z, Albahri OS, Zaidan A, Alnoor A, Alamoodi AH, Zaidan B (2021) How smart is e-tourism? a systematic review of smart tourism recommendation system applying data management. Computer Science Review. 39:100337

    Google Scholar 

  39. Abbasi-Moud Z, Vahdat-Nejad H, Sadri J (2021) Tourism recommendation system based on semantic clustering and sentiment analysis. Expert Syst Appl 167:114324

    Google Scholar 

  40. Wu X, Chen C (2022) Spatial distribution and accessibility of high level scenic spots in inner mongolia. Sustainability. 14(12):7329

    Google Scholar 

  41. Weng G, Li H, Li Y (2023) The temporal and spatial distribution characteristics and influencing factors of tourist attractions in chengdu-chongqing economic circle. Environ Dev Sustain 25(8):8677–8698

    Google Scholar 

  42. Chen L, Cao J, Zhu G, Wang Y, Liang W (2021) A multi-task learning approach for improving travel recommendation with keywords generation. Knowledge Based Systems. 233:107521

    Google Scholar 

  43. He S et al (2022) Research on tourism route recommendation strategy based on convolutional neural network and collaborative filtering algorithm. Security and Communication Networks. 2022

  44. Li H, Qiao M, Peng S (2022) Research on the recommendation algorithm of rural tourism routes based on the fusion model of multiple data sources. Discret Dyn Nat Soc 2022:1–10

    Google Scholar 

  45. Cepeda-Pacheco JC, Domingo MC (2022) Deep learning and internet of things for tourist attraction recommendations in smart cities. Neural Comput Appl 34(10):7691–7709

    Google Scholar 

  46. Cepeda-Pacheco JC, Domingo MC (2022) Deep learning and internet of things for tourist attraction recommendations in smart cities. Neural Comput Appl 34(10):7691–7709

    Google Scholar 

  47. Zhang R, Yao E, Liu Z (2017) School travel mode choice in beijing, china. J Transp Geogr 62:98–110

    Google Scholar 

  48. Agag GM, El-Masry AA (2017) Why do consumers trust online travel websites? drivers and outcomes of consumer trust toward online travel websites. J Travel Res 56(3):347–369

    Google Scholar 

  49. Zhang Y, Du J, Ma X, Wen H, Fortino G (2021) Aspect-based sentiment analysis for user reviews. Cogn Comput 13(5):1114–1127

    Google Scholar 

  50. Hammad AA, El-Halees A (2013) An approach for detecting spam in arabic opinion reviews. The International Arab Journal of Information Technology. 12

  51. Li J, Fan Q, Zhang K (2007) Keyword extraction based on tf/idf for chinese news document. Wuhan Univ J Nat Sci 12(5):917–921

    Google Scholar 

  52. Ke J, Wang W, Chen X, Gou J, Gao Y (2023) Jin S (2023) Medical entity recognition and knowledge map relationship analysis of chinese emrs based on improved bilstm-crf. Comput Electr Eng 108:108709

    Google Scholar 

  53. Zhang C, Xiang Y, Hao W, Li Z, Qian Y, Wang Y (2023) Automatic recognition and classification of future work sentences from academic articles in a specific domain. J Informet 17(1):101373

    Google Scholar 

  54. Zhang X, Li Y, Wang X, Liu F, Wu Z, Cheng X, Jiao L (2023) Multi-source interactive stair attention for remote sensing image captioning. Remote Sensing. 15(3):579

    Google Scholar 

  55. Li W, Ye P, Yu K, Min X, Xie W (2023) An abnormal surgical record recognition model with keywords combination patterns based on textrank for medical insurance fraud detection. Multimedia Tools and Applications. 1–15

  56. Xiong H, Wu G, Xue S, Li H, Zhu T (2021) Dictionary-based classical chinese word segmentation and its application on imperial edicts of jin dynasties. In: International Conference on Human Centered Computing. pp. 153–160. Springer

  57. Eligüzel N (2023) Analyzing society anti-vaccination attitudes towards covid-19: combining latent dirichlet allocation and fuzzy association rule mining with a fuzzy cognitive map. Fuzzy Optimization and Decision Making. 1–28

  58. Venugopal M, Sharma VK, Sharma K (2023) Web information mining and semantic analysis in heterogeneous unstructured text data using enhanced latent dirichlet allocation. Concurrency and Computation: Practice and Experience. 7410

  59. Gao Q, Wang W, Huang L, Yang X, Li T, Fujita H (2023) Dual-grained human mobility learning for location-aware trip recommendation with spatial–temporal graph knowledge fusion. Information Fusion. 92:46–63

  60. Wang Y, Qi L, Dou R, Shen S, Hou L, Liu Y, Yang Y, Kong L (2023) An accuracy-enhanced group recommendation approach based on dematel. Pattern Recogn Lett 167:171–180

    Google Scholar 

  61. Houshmand-Nanehkaran F, Lajevardi SM, Mahlouji-Bidgholi M (2022) Optimization of fuzzy similarity by genetic algorithm in user-based collaborative filtering recommender systems. Expert Syst 39(4):12893

    Google Scholar 

  62. Singh PK, Sinha S, Choudhury P (2022) An improved item-based collaborative filtering using a modified bhattacharyya coefficient and user–user similarity as weight. Knowl Inf Syst 64(3):665–701

  63. Ahmadian S, Ahmadian M, Jalili M (2022) A deep learning based trust-and tag-aware recommender system. Neurocomputing 488:557–571

    Google Scholar 

  64. Liu H, Zheng C, Li D, Zhang Z, Lin K, Shen X, Xiong NN, Wang J (2022) Multi-perspective social recommendation method with graph representation learning. Neurocomputing 468:469–481

    Google Scholar 

  65. Chao IM, Golden BL, Wasil EA (1996) The team orienteering problem. Eur J Oper Res 88(3):464–474

    Google Scholar 

  66. Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G, Vathis N (2015) Heuristics for the time dependent team orienteering problem: Application to tourist route planning. Computers & Operations Research. 62:36–50

    MathSciNet  Google Scholar 

  67. Vansteenwegen P, Souffriau W, Van Oudheusden D (2011) The orienteering problem: A survey. Eur J Oper Res 209(1):1–10

    MathSciNet  Google Scholar 

  68. Bin C, Gu T, Sun Y, Chang L, Sun L (2019)A travel route recommendation system based on smart phones and iot environment. Wireless Communications and Mobile Computing. 2019

  69. Chang L, Chen W, Huang J, Bin C, Wang W (2021) Exploiting multi-attention network with contextual influence for point-of-interest recommendation. Appl Intell 51:1904–1917

    Google Scholar 

  70. Tahmasebi F, Meghdadi M, Ahmadian S, Valiallahi K (2021) A hybrid recommendation system based on profile expansion technique to alleviate cold start problem. Multimedia Tools and Applications. 80:2339–2354

    Google Scholar 

  71. Cui Z, Xu X, Fei X, Cai X, Cao Y, Zhang W, Chen J (2020) Personalized recommendation system based on collaborative filtering for iot scenarios. IEEE Trans Serv Comput 13(4):685–695

    Google Scholar 

  72. Kotkov D, Veijalainen J, Wang S (2020) How does serendipity affect diversity in recommender systems? a serendipity-oriented greedy algorithm. Computing 102:393–411

    MathSciNet  Google Scholar 

  73. Bin C, Gu T, Sun Y, Chang L (2019) A personalized poi route recommendation system based on heterogeneous tourism data and sequential pattern mining. Multimedia Tools and Applications. 78:35135–35156

    Google Scholar 

  74. Gu T, Liang H, Bin C, Chang L (2021) Combining user-end and item-end knowledge graph learning for personalized recommendation. Journal of Intelligent & Fuzzy Systems. 40(5):9213–9225

    Google Scholar 

  75. Zhu G, Bin C, Gu T, Chang L, Sun Y, Chen W, Jia Z (2019) A neural user preference modeling framework for recommendation based on knowledge graph. In: PRICAI 2019: Trends in Artificial Intelligence: 16th Pacific Rim International Conference on Artificial Intelligence, Cuvu, Yanuca Island, Fiji, August 26–30, 2019, Proceedings, Part I 16, pp. 176–189. Springer

  76. Bin C, Gu T, Jia Z, Zhu G, Xiao C (2020) A neural multi-context modeling framework for personalized attraction recommendation. Multimedia Tools and Applications. 79:14951–14979

    Google Scholar 

Download references

Funding

This research was supported by the national social science fund of China “Research on the enhancement of logistics service quality and low-carbon governance mechanisms”, grant number 21FGLB046, and in part by the Graduate Science and Technology Innovation Project of Capital University of Economics and Business under 2023KJCX062.

Author information

Authors and Affiliations

  1. School of Management and Engineering, Capital University of Economics and Business, Beijing, 100070, China

    Kaibo Liang & Huwei Liu

  2. School of Information, Beijing Wuzi University, Beijing, 101149, China

    Man Shan, Xiaolan Li & Li Zhou

  3. School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China

    Junhui Zhao

Authors
  1. Kaibo Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huwei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Man Shan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junhui Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaolan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Li Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Funding acquisition, L.Z.; supervision, J.Z.; validation, H.L.; writing-original draft, K.L.; writing-review and editing, M.S. and L.L. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Huwei Liu.

Ethics declarations

Conflicts of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Additional information

Publisher's Note

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

Appendix

Appendix

Table 24 Spot feature tag library (Factor: Tour theme, part 1)
Full size table
Table 25 Spot feature tag library (Factor: Tour theme, part 2)
Full size table
Table 26 Spot feature tag library (Other factors)
Full size table
Table 27 Fuzzy affiliation of attraction feature labels
Full size table
Table 28 10 user behavior records indicate
Full size table
Table 29 User-interest feature label matrix construction results (Part 1)
Full size table
Table 30 User-interest feature label matrix construction results (Part 2)
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liang, K., Liu, H., Shan, M. et al. Enhancing scenic recommendation and tour route personalization in tourism using UGC text mining. Appl Intell 54, 1063–1098 (2024). https://doi.org/10.1007/s10489-023-05244-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-023-05244-6

Keywords

Search

Navigation