Advertisement

Developing Knowledge Based Recommender System for Tourist Attraction Area Selection in Ethiopia: A Case Based Reasoning Approach

  • Tamir Anteneh Alemu
  • Alemu Kumilachew Tegegne
  • Adane Nega Tarekegn
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 244)

Abstract

A knowledge based recommender reasons about the fit between a user’s need and the features of available products and it uses knowledge about users and products to pursue knowledge based approach to generate a recommendation, reasoning about what products/services meet the user’s requirements. Providing an effective service in the Tourism sector of Ethiopia 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. For the development of case based recommender system, essential knowledge was acquired through semi-structured interview and document analysis. Domain experts and visitors were interviewed to elicit the required knowledge about the selection process of attraction area. The acquired knowledge was modeled using hierarchical tree structure and it was represented using feature value case representation. At the end, jCOLIBRI programming tool was used to implement the system. The main data source (case base) used to develop case based recommender system for tourist attraction area selection is previous tourist cases collected from national tour operation and ministry of culture and tourism. As a retrieval algorithm, nearest neighbor retrieval algorithm is used to measure the similarity of new case (query) with cases in the case base. Accordingly, if there is a similarity between the new case and the existing case, the system assigns the solution (recommended attraction area and visiting time) of previous case as a solution to new case. To decide the applicability of the prototype system 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.

Keywords

Recommender system Case based reasoning Tourism in Ethiopia 

References

  1. 1.
    Edmunds, A., Morris, A.: The problem of information overload in business organizations. Department of Information Science, Southborough University (2000)Google Scholar
  2. 2.
    Burke, R.: Hybrid web recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-72079-9_12CrossRefGoogle Scholar
  3. 3.
    Culture and Tourism Office: Tourism Development Strategy, Addis Ababa, Ethiopia (2011)Google Scholar
  4. 4.
    Ethiopia T.: Application of Case-Based Reasoning for Amharic Legal Precedent Retrieval: A Case Study with the Ethiopian Labor Law. Addis Ababa University, Ethiopia (2002)Google Scholar
  5. 5.
    Lorenzi, F., Ricci, F.: Case-based recommender systems: a unifying view. In: Mobasher, B., Anand, S.S. (eds.) ITWP 2003. LNCS (LNAI), vol. 3169, pp. 89–113. Springer, Heidelberg (2005).  https://doi.org/10.1007/11577935_5CrossRefGoogle Scholar
  6. 6.
    Mehiret, Y.: Tourism certification as a tool for promoting sustainability in the Ethiopian tourism industry. Addis Ababa University, Addis Ababa, Ethiopia (2011)Google Scholar
  7. 7.
    Ministry of Culture and Tourism: Manuals of Ethiopian tourism guide, Addis Ababa, Ethiopia (2012)Google Scholar
  8. 8.
    Main, J., et al.: A tutorial on case based reasoning. In: Pal, S.K., Dillon, T.S., Yeung, D.S. (eds.) Soft Computing in Case Based Reasoning, pp. 1–28. Springer, London (2001).  https://doi.org/10.1007/978-1-4471-0687-6_1CrossRefGoogle Scholar
  9. 9.
    Shimazu, H.: ExpertClerk: a conversational case-based reasoning tool for developing salesclerk agents in E-commerce webshops. Artif. Intell. Rev. 18(3–4), 223–244 (2002)CrossRefGoogle Scholar
  10. 10.
    Bergmann, R.: Introduction to case-based reasoning. Department of Computer Science University of Kaiserslautern (1998)Google Scholar
  11. 11.
    Prentza, J., Hatzilygeroudi, I.: Categorizing approaches combining rule-based and case-based reasoning. Department of Computer Engineering and Informatics, School of Engineering, University of Patras (2007)Google Scholar
  12. 12.
    United Nations World Tourism Commission: Tourism Highlights. 2007 Edition (2007)Google Scholar
  13. 13.
    Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)Google Scholar
  14. 14.
    Fong, S., Biuk-Aghai, R.: An automated admission recommender system for secondary school student. In: The 6th International Conference on Information Technology and Application (2009)Google Scholar
  15. 15.
    Satyanarayana, K., Rajagoplan, S.P.: Recommender system for educational institutions. Asian J. Inf. Technol. 6, 964–969 (2007)Google Scholar
  16. 16.
    Bendakir, N., Aïmeur, E.: Using association rules for course recommendation. Am. Assoc. Artif. Intell. (2006)Google Scholar
  17. 17.
    Salem, A.B.M., et al.: A case base experts system for diagnosis of heart disease. Int. J. Artif. Intell. Mach. Learn. 5(1), 33–39 (2005)Google Scholar
  18. 18.
    Bergmann, R., et al.: A representation in case based reasoning. Knowl. Eng. Rev. 20, 1–4 (2005)CrossRefGoogle Scholar
  19. 19.
    Burke, R.: Knowledge based recommender systems. University of California, Irvine (2006)Google Scholar
  20. 20.
    Recio, J.A., et al.: jCOLIBRI 1.0 in a nutshell. A software tool for designing CBR systems (2002)Google Scholar
  21. 21.
    Sagheb-Tehrani, M., et al.: A Conceptual model of knowledge elicitation, college of business. Technology and Communication, p. 2 (2009)Google Scholar
  22. 22.
    Getachew, W.: Application of case-based reasoning for anxiety disorder diagnosis, Addis Ababa University, Ethiopia (2012)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Tamir Anteneh Alemu
    • 1
  • Alemu Kumilachew Tegegne
    • 1
  • Adane Nega Tarekegn
    • 1
  1. 1.Faculty of Computing Bahir Dar Institute of TechnologyBahir Dar UniversityBahir DarEthiopia

Personalised recommendations