Recommender System in Tourism Using Case based Reasoning Approach

Full Text (PDF, 678KB), PP.34-43

Views: 0 Downloads: 0


Tamir Anteneh Alemu 1,* Alemu Kumilachew Tegegne 1 Adane Nega Tarekegn 1

1. Faculty of Computing, Bahir Dar Institute of Technology, Bahir Dar, Ethiopia

* Corresponding author.


Received: 7 Apr. 2017 / Revised: 26 Apr. 2017 / Accepted: 13 May 2017 / Published: 8 Sep. 2017

Index Terms

Recommender system, case based reasoning (CBR), tourism, Ethiopia


Using recommender systems with the help of computer systems technology to support the Tourist advising process offers many advantages over the traditional system. A knowledge based recommender reasons about the fit between a user’s need and the features of available products. Providing an effective service in Ethiopian Tourism sector is critical to attract more foreign and local tourists. However, there are major problems that need immediate solution. First, the difficulty of getting fast, reliable, and consistent expert advice in the sector that is suitable to each visitor’s characteristics and capabilities. Second, inadequacy of the number of experienced experts and consulting individuals who can give advice on tourism issues in the country. Therefore, this paper aims to design a recommender system for tourist attraction area and visiting time selection that can assist experts and tourists to make timely decisions that helps them to get fast and consistent advisory service. So that, visitors can identify tourist attraction areas that have the highest potential of success/satisfaction and that match their personal characteristics. The system provides recommendation to visitors based on previously solved cases and new query given by the tourist. For this study, about 615 cases which are collected from National Tour operation and 10 attributes which are collected from experts are used as case base. These attributes and cases are used as knowledge base to construct case based recommender. The system calculates similarity between existing cases and new queries that are provided by the visitors, and provide solution or recommendation by taking best cases to the new query. In this study, JCOLIBRI case base development tool is used to develop the prototype. JCOLIBRI contains both user interface which enables visitors to enter their query and programming codes with the help of Java script language. To decide the applicability of the prototype in the domain area, the system has been evaluated by involving domain experts and visitors through visual interaction using the criteria of easiness to use, time efficiency, applicability in the domain area and providing correct recommendation. Based on prototype user acceptance testing, the average performance of the system is 80% and 82% by domain experts and visitors respectively. The performance of the system is also measured using the standard measure of relevance (IR system) recall, precision and accuracy measures, where the system registers 83% recall, 61% precision and 85.4% accuracy.

Cite This Paper

Tamir Anteneh Alemu, Alemu Kumilachew Tegegne, Adane Nega Tarekegn, "Recommender System in Tourism Using Case based Reasoning Approach", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.9, No.5, pp. 34-43, 2017. DOI:10.5815/ijieeb.2017.05.05


[1]Angela Edmunds, Anne Morris (2000). The problem of information overload in business organizations: Department of Information Science, Loughborough University.
[2]Burke, R. (2007). Hybrid Web Recommender Systems, University of California, Irvine.
[3]Culture and tourism office (2011). Tourism Development Strategy, Ethiopia, Addis Ababa.
[4]Ethiopia T., (2002). Application of Case-based Reasoning for Amharic Legal Precedent Retrieval: A Case Study with the Ethiopian Labor Law, Addis Ababa University, Ethiopia.
[5]Fabiana, et al (2003). Case-based recommender systems: A unifying view, in Intelligent Techniques for Web Personalization, IJCAI 2003 Workshop, p. 2-10.
[6]YechaleMehiret (2011). Tourism certification as a tool for promoting sustainability in the Ethiopian tourism industry. Addis Ababa University, Addis Ababa, Ethiopia.
[7][Ministry of culture and tourism (2012). Manuals of Ethiopian tourism guide, Ethiopia, Addis Ababa.
[8]Main, et al (2001). A tutorial on case based reasoning, pp.1-28.
[9]Shimazu (2002). A Conversational Case-based Reasoning Tool for Developing Salesclerk Agents in E-Commerce Webshops. Artificial Intelligence Review 18 (3-4), 223–244.
[10]Ralph Bergmann (1998). Introduction to Case-Based Reasoning, Department of Computer Science University of Kaiserslautern.
[11]Prentza andHatzilygeroudi (2007). Categorizing approaches combining rule-based and case-based reasoning, Department of Computer Engineering and Informatics, School of Engineering, University of Patras.
[12]United Nations World Tourism Commission (2007). Tourism Highlights. 2007 Edition.
[13]Aamodt, A., Plaza, E. (1994). Case-Based Reasoning: Foundational Issues, Methodological
[14]Variations and System Approaches. AI Communications. IOS Press, Vol. 7: 1, pp. 39-59.
[15]Fong, S. and Biuk-Aghai, R. (2009). An Automated Admission Recommender system for Secondary School Student.The 6th international conference on Information Technology and application.
[16]Satyanarayana, K. and Rajagoplan, S.P. (2007). Recommender system for Educational Institutions.Asian Journal of Information Technology. M.G.R University, India.
[17]Bendakir, N. and ─▒meur, E. (2006). Using Association Rules for Course Recommendation.American Association for Artificial Intelligence.
[18]Salem, et al (2005). A Case Base Experts System for Diagnosis of Heart Disease. International Journal on Artificial Intelligence and Machine Learning, 5(1), pp. 33-39.
[19]Bergmann, et al (2005). a representation in case based reasoning, The Knowledge Engineering Review, Vol. 00:0, 1–4.
[20]Burke, R. (2006). Knowledge based recommender systems, University of California, Irvine.
[21]Juan A., et al (2002). jCOLIBRI 1.0 in a nutshell. A software tool for designing CBR systems.
[22]Tehrani, et al (2009). A Conceptual Model of Knowledge Elicitation, College of Business, Technology and Communication, pp. 2.
[23]Getachew W. (2012).Application of case-based reasoning for anxiety disorder diagnosis, Addis Ababa University, Ethiopia.