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

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.