Abstract
The travel industry's contribution to sustainable development is noteworthy, since it offers travel experiences that are mutually beneficial to both local populations and tourists. But in order to maximize multiple resources in social tourism, an intelligent information system (IIS) is necessary for giving valuable tourism recommendations. Such systems use cutting-edge technologies like artificial intelligence, machine learning, and data analytics to improve the social tourism sector's overall efficacy and efficiency. The proposed intelligent information recommender system (IIRS) combines a number of different components to handle a number of tourism-related issues, such as impact assessment, community involvement, travel planning, and destination selection with the purpose of promoting sustainable social tourism. Fuzzy C-means clustering (FCM) is used in the proposed system for extraction of features. After extraction of features, the model is trained and tested using ensemble machine learning classifiers such as decision tree (DT) and extreme gradient boosting (XGB). Lastly, the evaluation of the system can be done with distinguished parameters such as accuracy, precision, recall, and F1-score.
Similar content being viewed by others
Explore related subjects
Find the latest articles, discoveries, and news in related topics.Avoid common mistakes on your manuscript.
1 Introduction
The tourism industry offers distinct benefits for a country such as economic growth, revenue generation, create jobs, and provide benefits to the local communities [1, 2]. Social tourism [3] is the fastest growing field in which building of positive relationship between the local host and tourist is most important factor. Social tourism is kind of working holiday in which a tourist can visit a place and do work based on his interest for the local host. On the other hand, local host providing a best environment for visit the place, food, and lodging. The major aim of social tourism is to encourage the tourist for visit the place in respect to utilized their skillset and obtained the maximum benefits to the visitors. The advantages of social tourism include supports for local projects, building relationships, helping children, donations, and support to the senior citizens. It also includes support for accessible travelling for all communities even if they cannot afford the visit [4]. The tourism sector has established itself as one of the most lucrative and economically stimulating industries for any nation. Numerous tourism-related sub-fields call for greater accuracy and precision; if they are attained, the industry could experience explosive growth. Machine learning [5] in the tourism sector reduces confusion and produces a more practical method of offering the sector's more advanced capabilities. Machine learning is the process by which computers pick up knowledge from many forms of data to produce accurate predictions in a variety of disciplines, such as demand forecasting, enhance accuracy, etc. A tourist recommendation system is thought to be a useful tool for fostering relationships and communication between travellers [6, 7]. The recommender system compares the information provided by the tourist destinations or the information/reviews provided by the visitors, use specific algorithms, do calculations, and generate a list of suggested attractions for the visitor.
1.1 Novelty and contributions of this study
-
An intelligent tourism recommendation system is proposed in the given paper with an aim to foster sustainable tourism industry.
-
The dataset taken into consideration is cleaned using a complimentary filter, which is a computationally inexpensive sensor fusion technique that consists of a low-pass and a high-pass filter. These filters provide reliable tourism recommendations by reducing redundant data.
-
Then, the cleaned data is subjected to fuzzy c-means clustering (FCM) for feature extraction. FCM is chosen because this algorithm can be used to identify tourists who have mixed preferences or interests based on their similarity to each cluster.
-
Ensemble machine learning classifiers are used for classification, namely decision trees (DT) and XGB. The former is chosen because it provides tourists with a wide range of options and investigates its possible outcomes, whereas the latter is highly scalable and produces the minimum loss for the dataset.
1.2 Organization
The remaining sections of the paper includes: In Sect. 2, the literature review in the field of a sophisticated recommender system for social tourism is analyzed. We have outlined a technique with the system's process in Sect. 3. The results and their explanation in terms of the evaluation criteria are covered in Sect. 4. The paper is then concluded with future scope in Sect. 5 followed by references.
2 Literature review
This section is focused on the works conducted by several researchers in the area of tourism recommendation system as shown in Table 1.
3 Proposed system
Following components are included in the proposed tourism recommendation system as shown in Figure 1.
3.1 Pre-processing of data
It involves the removal of errors, inconsistencies, and outliers that can adversely affect the system’s performance. It also involves normalizing data values for further queries and analysis. To deal with oversampling and under sampling, we have considered the synthetic minority oversampling (SMOTE) technique [16] for balancing the dataset values.
3.2 Data cleaning using complimentary filter
The complementary filter is a technique commonly used in sensor fusion and signal processing to combine the outputs of multiple sensors with different characteristics [17]. Firstly, determine the sensors type or data sources using complementary information. Then evaluate the weight factor of each data sources or sensors based on the accuracy. It is followed by applying the complementary filter method on the dataset using a formula (Equation 1):
3.3 Extraction of features using fuzzy c-means clustering (FCM)
This algorithm assigns data points to multiple clusters with varying degrees of membership based on their similarity to each cluster. In the context of tourism, it can be used to identify tourists who have mixed preferences or interests by computing clusters at each data points. Figure 2 shows the flowchart of working of FCM.
We have considered a dataset D = {D1, D2, ………Dq} with a set of clusters C= {C1, C2, …….., Cp} and some set of membership values M = {1<P<m, 1<Q<n} that is required to be formulated in a manner such that train values can combine neural network with FCM [16]. Equation 2 shows the efficient auto-encoder values by minimizing the training set:
It is used to enhance the performance of the system that can be represented as in Equation 3 such as:
Evaluate the cluster center and update the membership matrix accordingly with an Equation 4:
While the membership matrix can be defined with Equation 5 such as:
3.4 Classification using DT and XGB
The given system is trained and tested by using ensemble machine learning classifiers, namely decision trees (DT) and XGB. The former is chosen because it provides tourists with a wide range of options and investigates its possible outcomes, whereas the latter is highly scalable and produces the minimum loss for the dataset. The confusion matrices related to both the classifiers are shown in next section.
4 Results and discussions
The dataset is collected from the UNWTO [18], which provides a questionnaire series based on the specific guidelines of the UN. The dataset contains information about the tourist's travel plans, including their accommodation and expenditure details during travel.
The model is implemented using python that includes distinct libraries such as sklearn, keras, numpy, pandas, etc. To do so, we have applied several fundamental pre-processing steps with the dataset such as extraction of useful features, filling of the missing values, removal of null records, and perform exploratory analysis of the data based on distinct features. The correlation among these parameters is depicted by correlation heatmap [19].
Figure 3 shows the heatmap that indicates the correlation among the parameters. Figure 4 and 5 shows confusion matrix for testing and training dataset showing number of actual and predicted travel plans purchased using DT and XGB respectively.
Figure 6 shows representation of dataset attributes when trained and tested using DT classifier.
4.1 Comparative analysis
The given section presents the comparative analysis showing how the proposed system outperforms existing recent studies [8,9,10,11,12,13,14,15] in terms of accuracy, precision, recall, and f1 score. Table 2 displays the comparison between the proposed system and previously published studies.
The comparison study indicates that the proposed system (FCM + DT + XGB) outperforms other techniques used in the current state of the art in terms of accuracy, precision; recall and f1 score [8,9,10,11,12,13,14,15]. The graphical comparison is shown in Fig. 7. Of all the techniques, the proposed system provides the highest accuracy (87.45%), highest recall (86.55%), highest precision (85.37%), and highest f1 score (85.12%).
5 Conclusion and future scope
The development of an intelligent information recommender system (IIRS) for the next generation of sustainable social tourism holds immense potential for advancing the industry's goals of sustainability, responsible travel, and community engagement. It ensures that tourists have access to meaningful travel experiences while contributing positively to local communities and the environment. The proposed intelligent information recommender system (IIRS) combines a number of different components to handle a number of tourism-related issues, such as impact assessment, community involvement, travel planning, and destination selection with the purpose of promoting sustainable social tourism. Fuzzy C-means clustering (FCM) is used in the proposed system for extraction of features. After extraction of features, the model is trained and tested using ensemble machine learning classifiers such as decision tree (DT) and extreme gradient boosting (XGB). Followed by this, the performance of the system is validated based on evaluation metrics such as accuracy, precision, recall and f1 score. The results show that the best performance is achieved by our proposed work as compared to existing recent works.
As a future scope, the distinct dataset parameters can be integrated with designing of an IoT smart tourism system for ensuring personalized recommendations to travellers related to latest tour packages at affordable prices. The IoT system would make use of sensors for collecting relevant information related to tourism and process that information by applying sentiment analysis to produce personalized recommendations.
References
Song Y, He Y (2023) Toward an intelligent tourism recommendation system based on artificial intelligence and IoT using Apriori algorithm. Soft Comput 27:19159–19177. https://doi.org/10.1007/s00500-023-09330-2
Ma H (2024) Development of a smart tourism service system based on the Internet of Things and machine learning. J Supercomput 80:6725–6745. https://doi.org/10.1007/s11227-023-05719-w
Guo Z (2021)"Research on intelligent recommendation method of rural tourism route," 2021 6th International Conference on Smart Grid and Electrical Automation (ICSGEA), Kunming, China, pp. 242–247, https://doi.org/10.1109/ICSGEA53208.2021.00060.
Narula GS, Wason R, Jain V, Baliyan A (2018) Ontology mapping and merging aspects in semantic web. Int Rob Auto J 4(1):00087. https://doi.org/10.15406/iratj.2018.04.00087
Deepak S, Ojha A, Acharjya K et al (2024) A novel and proposed triad machine learning-based framework for the prognosis of Huntington’s disease. Int J Inf Tecnol. https://doi.org/10.1007/s41870-023-01719-4
Boopathi M, Parikh S, Awasthi A et al (2024) OntoDSO: an ontological-based dolphin swarm optimization (DSO) approach to perform energy efficient routing in Wireless Sensor Networks (WSNs). Int j inf tecnol 16:1551–1557. https://doi.org/10.1007/s41870-023-01698-6
Susanty A, Puspitasari NB, Rosyada ZF et al (2024) Design of blockchain-based halal traceability system applications for halal chicken meat-based food supply chain. Int J Inf Tecnol 16:1449–1473. https://doi.org/10.1007/s41870-023-01650-8
Meng L (2024) The Convolutional Neural Network Text Classification Algorithm in the Information Management of Smart Tourism Based on Internet of Things. IEEE Access 12:3570–3580. https://doi.org/10.1109/ACCESS.2024.3349386
Chand R, Nijjer S, Jandwani A et al (2024) A novel funnel and ontological mechanism for sustainable Green Human Resource Management (GHRM). Int j inf tecnol 16:369–374. https://doi.org/10.1007/s41870-023-01622-y
Kaur I, Narula GS, Wason R et al (2018) Neuro fuzzy—COCOMO II model for software cost estimation. Int J Inf Tecnol 10:181–187. https://doi.org/10.1007/s41870-018-0083-6
Ambikapathy A, Sandilya J, Tiwari A, Singh G, Lochan Varshney”, (2021) Analysis of object following robot module using android, Arduino and Open CV, Raspberry Pi with OpenCV and Color Based Vision Recognition. In: Priyadarshi N et al (eds) Advances in Power Systems and Energy Management: Select Proceedings of ETAEERE 2020. Springer, Singapore, pp 365–377
Wason R (2018) Deep learning: evolution and expansion. Cogn Syst Res. https://doi.org/10.1016/j.cogsys.2018.08.023
Dey S, Shukla D (2020) "Analytical study on use of AI techniques in tourism sector for smarter customer experience management," 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), Gunupur, India, pp. 1–5, https://doi.org/10.1109/ICCSEA49143.2020.9132925
Sarkar JL, Majumder A, Panigrahi CR et al (2023) Tourism recommendation system: a survey and future research directions. Multimed Tools Appl 82:8983–9027. https://doi.org/10.1007/s11042-022-12167-w
Santoso AJ, Soares JD (2017) "M-guide: hybrid recommender system tourism in east-timor,"2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT), Denpasar, Indonesia, pp. 303–309, https://doi.org/10.1109/ICSIIT.2017.16.
Bi F, Liu H (2022) Machine learning-based cloud IOT platform for intelligent tourism information services. J Wireless Com Network 2022:59. https://doi.org/10.1186/s13638-022-02138-y
H. Zacarias, G. Cangondo, L. Souza-Pereira, N. M. Garcia, B. Silva and N. Pombo, "Application of Content-Base Recommendation Algorithms on Mobile Travel Applications," 2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC), Jeddah, Saudi Arabia, 2023, pp. 1–5, https://doi.org/10.1109/ICAISC56366.2023.10085680
https://www.unwto.org/tourism-statistics/tourism-statistics-database (accessed in April, 2024)
Yamamoto K, Ikeda T (2017) Social recommendation gis for urban tourist spots,"2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Sydney, NSW, Australia, pp. 50–57, https://doi.org/10.1109/HPCC-SmartCity-DSS.2016.0018
Frikha M, Mhiri M, Gargouri F (2017) "Using social interaction between friends in knowledge-based personalized recommendation," 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), Hammamet, Tunisia, pp. 1454–1461, https://doi.org/10.1109/AICCSA.2017.206.
Funding
The writers have not received any financial support from the public, commercial, or not-for-profit sectors.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflicts of interest to report regarding the present study.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Kumar, A., Goyal, H.R. & Sharma, S. An intelligent information recommender system (IIRS) for next generation sustainable social tourism industry. Int. j. inf. tecnol. 16, 3411–3418 (2024). https://doi.org/10.1007/s41870-024-01941-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41870-024-01941-8