Design and Development of a Tourism Recommender System using Volunteered Geographic Information, Case Study: Yazd

Document Type : Research Article


1 PhD Student in Remote Sensing and Geographic Information System, University of Tehran, Tehran, Iran

2 Associate Professor of Remote Sensing and Geographic Information System, University of Tehran, Tehran, Iran


Extended Abstract
Tourism has positive economic, social, and cultural effects on human societies and is one of the most valuable sources of income in many countries. Travel planning is one of the most important issues that can be considered in order to have a good and desirable trip. Travel recommendation systems are examples of techniques used in the field of tourism that aim to match the characteristics of tourism resources or tourist attractions with the needs and priorities of tourists and provide the most appropriate tourism place. Voluntary geographic information (VGI) can play an important role in this regard. People in the community can monitor and share the geographic information of their environment as active, analytical, intelligent, responsible, environment-aware, circulate, distributed and interactive sensors.
This study recommends a system that intelligently receives demographic information, users' interests and preferences, and according to the spatial information shared as a VGI by other tourists, recommends the most suitable tourist attractions to the visitors using the recommendation system based on Bayesian networks. Also, the tourism-oriented GIS system, using the TSP algorithm, provides the most optimal route to reach the tourist attractions on the map.
The study area is the historical area of ​​Yazd with an approximate area of ​​3.5 square kilometers, which is located in the city center. Yazd city, with an area of ​​107.4 square kilometers, is the largest historical unit and administrative center of Yazd province, which in recent years has faced a very large population growth compared to other urban areas of Yazd province.
In this research, the database of Yazd Municipality has been used to collect statistical information on tourism visits in 2015, the vector layer of the regional road network and the vector layers related to the location of tourist places, services and all related facilities. Also, in order to implement the tourism recommender system with a suitable user interface, software programs such as Arc GIS 10.5, PostgreSQL 9.6.1, Visual Studio 2013, NETICA 6.3, Notepad ++, and Geo-Server 2.13.2 have been exerted.
Bayesian network is one of the methods of presenting knowledge by combining Bayesian theory with a graphical model and has many applications in problem-solving and cause and effect analysis. This method uses a posterior probability distribution to analyze various parameters and includes a set of nodes and directional edges that nodes represent variables and edges represent cause and effect relationships between variables.
The Bayesian network designed in this study has been qualified and quantified based on demographic information (age, gender, income, occupation, level of education and type of travel of users), interests and preferences of tourists, and previous knowledge (information collected from related sources) in the NETICA software.
VGI is an essential source of geographic information when data collected from other sources is impossible. The information system designed in this research has a system to enter the preferences and interests of tourists after visiting tourist attractions. In this system, the experience of others is considered more than the experience of new people.
In addition to providing a list of tourist destinations tailored to users' preferences, a tourism recommender system also identifies the best route to access each tourist attraction. The routes suggested in this study start by moving from a tourist residence or hotel and return to the starting point after visiting the recommended places. For this reason, the TSP problem has been used for optimal routing in this system.
Results and discussion
This article aims to develop a tourism recommender system to offer suitable tourist attractions to tourists. The system can predict tourist attractions tailored to new users' interests and access paths based on combining the demographic information of new users with data from previous ones who voluntarily share their views of tourist attractions by composing recommendation algorithms and VGI data in the form of a Web-GIS system.
After introducing the tourist attractions, the user's movement route between the tourist attractions is done by specifying the starting point (Mehr Hotel). Also, in order to move the user, the suggested locations are extracted and the optimal route introduced by the system is displayed on the OSM map with the help of the network layer of the area roads. At any time, the user can view the nearest facilities on the map, such as coffee shops, restaurants, gas stations, hospitals, etc.
Determining the appropriate tourist attractions according to the interests and preferences of tourists is one of the important measures in tourism planning. Tourists will be delighted with a trip when their needs, interests, preferences, demographics, and social conditions are considered in the travel planning. In this research, a GIS web-based tourism recommender system has been created using voluntary geographic information and tourist demographic information. The system uses Bayesian network modeling to analyze the preferences of tourists and can predict the behavior of visiting tourist attractions through conditional probabilities and display the impact of each parameter and factor in selecting the type of tourist attractions. One of the most important features of this system is the possibility of user interaction so that tourists can voluntarily share their opinions and suggestions about the visited attractions. So, the system will provide recommendations related to tourist attractions that are in accordance with the interests and preferences of new tourists.


  1. جلوخانی نیارکی، محمدرضا (1395) طراحی و پیاده‌سازی سامانه پایش محیط‌زیست شهروند-محور مبتنی بر وب GIS، اولین کنفرانس ملی فناوری اطلاعات و مدیریت شهری، 18-17 اسفند 1395، تهران، صص. 5-1.
  2. جمالی، حسین؛ سجادی، ژیلا؛ رضویان، محمدتقی؛ حیدری، جهانگیر (1397) ارزیابی مؤلفه‌های تأثیرگذار بر رضایتمندی از مقاصد گردشگری (مطالعه موردی: شهرهای ساحلی استان بوشهر)، فصلنامه گردشگری شهری، دوره 5، شماره 3، صص. 64-49.
  3. جوانشیری، مژده؛ حنایی، تکتم؛ سیدالحسینی، سید مسلم؛ سعیدی مفرد، ساناز (1399) نقش ارزش‌های فرهنگی در اضطراب مسیریابی گردشگران مطالعه موردی: منطقه 8 شهرداری مشهد، فصلنامه گردشگری شهری، دوره 7، شماره 4، صص. 19-1.
  4. منتظری، مرجان و براتی، ناصر (1392) برنامه‌ریزی راهبردی توسعه گردشگری، رهیافتی کارآمد جهت تحقق گردشگری پایدار (مطالعه موردی: شهر یزد)، هفت شهر، دوره 4، شماره‌های 47 و 48، صص. 57-40.
  5. مودت، الیاس؛ ملکی، سعید؛ مؤمنی، کورش (1396) ارزیابی و سنجش ساختار فضایی و خزش شهری (مطالعه موردی: شهر یزد)، جغرافیای اجتماعی شهری، دوره 4، شماره 2، صص. 175-151.
  6. Abolhoseini, S. & Abbasi, O. R. & Tahani, N. (2018) Tour Planning for Separate Individuals: Individual Tourists Mobile System. In Adjunct Proceedings of the 14th International Conference on Location Based Services, January 15-17, ETH Zurich, pp.167-172.
  7. Adomavicius, G. & Tuzhilin, A. (2005) Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, Vol.17, No.6, pp.734-749.
  8. Bahramian, Z. & Abbaspour, R. A. & Claramunt, C. (2017) A Context-Aware Tourism Recommender System Based on a Spreading Activation Method. ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.333-339.
  9. Berhanu, M. & Raghuvanshi, T. K. & Suryabhagavan, K. (2017) Web-based GIS approach for tourism development in addis ababa city, Ethiopia. Malays J Remote Sens GIS, Vol.6, No.1, pp.13-25.
  10. Borràs, J. & Moreno, A. & Valls, A. (2014) Intelligent tourism recommender systems: A survey. Expert Systems with Applications, Vol.41, No.16, pp.7370-7389.
  11. Borzooei, Z. (2013) Thesis: WebGIS system in tourism industry and cultural institutions development related to Bijar city (Kordestan province), Department of Remote Sensing and GIS, Shahid Chamran University of Ahvaz.
  12. Bozanta, A. & Kutlu, B. (2017) Current state and future trends in location recommender systems, International Journal of Information Technology and Computer Science (IJITCS), Vol.9, No.6, pp.1-8.
  13. Cooper, A. K. & Coetzee, S. & Kaczmarek, I. & Kourie, D. G. & Iwaniak, A. & Kubik, T. (2011). Challenges for quality in volunteered geographical information. Africa GEO, Cape Town, South Africa, PP.34-38.
  14. Elwood, S. (2008) Volunteered geographic information: future research directions motivated by critical, participatory, and feminist GIS, Geo Journal, Vol.72, No.3, pp.173-183.
  15. Gao, M. & Liu, K. & Wu, Z. (2010) Personalisation in web computing and informatics: Theories, techniques, applications, and future research, Information Systems Frontiers, Vol.12, No.5, pp.607-629.
  16. Gómez-Barrón, J.-P. & Manso-Callejo, M.-Á. & Alcarria, R. & Iturrioz, T. (2016) Volunteered geographic information system design: Project and participation guidelines, ISPRS International Journal of Geo-Information, Vol.5, No.7, pp.1-35.
  17. González-Ramiro, A. & Gonçalves, G. & Sánchez-Ríos, A. & Jeong, J. S. (2016) Using a VGI and GIS-based multicriteria approach for assessing the potential of rural tourism in Extremadura (Spain), Sustainability, Vol.8, No.11, pp.1-15
  18. Goodchild, M. F. (2007) Citizens as sensors: the world of volunteered geography, Geo Journal, Vol.69, No.4, pp.211-221.
  19. Hauthal, E. & Burghardt, D. (2016) Using VGI for analyzing activities and emotions of locals and tourists. Paper presented at the Link-VGI workshop in connection with the AGILE, June 14-17, Helsinki, pp.1-6
  20. Hsu, F.-M. & Lin, Y.-T. & Ho, T.-K. (2012. Design and implementation of an intelligent recommendation system for tourist attractions: The integration of EBM model, Bayesian network and Google Maps, Expert Systems with Applications, Vol.39, No.3, pp.3257-3264.
  21. Huang, Y. & Bian, L. (2009) A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the Internet, Expert Systems with Applications, Vol.36, No.1, pp.933-943.
  22. jamali, H. & Sajadi, J. & Razavian, M. & heydari, J. (2018) Evaluation Efective Authors On satisfaction For tourist destinations The Case of coastal Cities of Boushehr province, Journal of Urban Tourism, Vol.5, No.3, pp.49-64. [in Persian].
  23. Javanshiri, M. & Hanaee, T. & Seyedolhosseini, S. & Saeedi Mofrad, S. (2021) The Role of Cultural Values in Tourist Wayfinding Anxiety Case Study: District 8 of Mashhad Municipality, Journal of Urban Tourism, Vol.7, No.4, pp.1-19. [in Persian].
  24. Jelokhani-Niaraki, Mohammad Reza. (2015) Design and implementation of citizen-based GIS web-based environmental monitoring system, First National Conference on Information Technology and Urban Management, March 7-8, Tehran, pp.1-5. [in Persian].
  25. Lu, J. & Wu, D. & Mao, M. & Wang, W. & Zhang, G. (2015) Recommender system application developments: a survey, Decision Support Systems, Vol.74, pp.12-32.
  26. Martınez, A. M. & Webb, G. I. & Chen, S. & Zaidi, N. A. (2016) Scalable learning of Bayesian network classifiers, Journal of Machine Learning Research, Vol.17, No.44, pp.1-35.
  27. Mavedat, E. & Maleki, S. & Momeni, K. (2017) Assessment and Evaluation the Spatial Structure and Urban Creep (Case Study: Yazd City), Journal of Urban Social Geography, Vol.4, No.2, pp.151-175. [in Persian].
  28. Mizutani, Y. & Yamamoto, K. (2017) A sightseeing spot recommendation system that takes into account the change in circumstances of users, ISPRS International Journal of Geo-Information, Vol.6, No.10, p.1-20.
  29. Mohammadzadeh, M. A. R. J. A. N. (2008) Developing a visitor decision support system for natural tourist destinations (Doctoral dissertation, Thesis (PhD). RMIT University, Melbourne Australia, OK. K. (2006) Multiple criteria activity selection for ecotourism planning in Igneada, Turk J Agric For, pp.153-164.
  30. Montazeri, M. & Barati, N. (2014) Tourism development strategic planning, efficient approach to achieve sustainable tourism (Case Study: Yazd). HAFTSHAHR, Vol.4, No.47 & 48, pp.40-57. [in Persian].
  31. Papić-Blagojević, N. & Gajić, T. & Đokić, N. (2012) Using Bayesian network and AHP method as a marketing approach tools in defining tourists' preferences, Turizam, Vol.16, No.1, pp.8-19.
  32. Ravi, L. & Vairavasundaram, S. (2016). A collaborative location based travel recommendation system through enhanced rating prediction for the group of users, Computational intelligence and neuroscience, pp.1-28.
  33. Ricci, F. (2002) Travel recommender systems, IEEE Intelligent Systems, Vol.17, No.6, pp.55-57.
  34. Rifki, M. & Rahmafitria, F. & Sugito, N. T. (2019) Tourism component evaluation: GIS based analysis towards the qualification of destination planning, Advances in Social Science, Education and Humanities Research, Vol.259, pp.121-124.
  35. Shinde, V. R. & Marathe, V. R. & Kamani, A. R. & Kalekar, P. A. (2017) Tour Plan Using Ontology, Formal Concept Analysis and Bayesian Analysis, Vol.3, No.3, pp.12-17.
  36. Shukla, Y. (2017) State of Art Survey of Travel based Recommendation System, International Journal of Advanced Research in Computer Science, Vol.8, No.3, pp.1098-1102.
  37. Wu, H. & He, Z. & Gong, J. (2010) A virtual globe-based 3D visualization and interactive framework for public participation in urban planning processes. Computers, Environment and Urban Systems, Vol.34, No.4, pp.291-298.
  38. Yeung, K. F. (2011) A context-aware framework for personalised recommendation in mobile environments, Doctoral dissertation, University of Portsmouth, pp.1-152.
  39. Zerihun, M. E. (2017) Web based GIS for tourism development using effective free and open source software case study: Gondor town and its surrounding area, Ethiopia, Journal of Geographic Information System, Vol.9, No.1, p.47-59.