Recommender System in Tourism Using Case based Reasoning Approach

Abstract

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.