| 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 the 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 aimed 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 were collected
from National Tour operation and 10 attributes which were collected from
experts were used as case base. These attributes and cases were used as
knowledge base to construct case base recommender. The system calculates
similarity between existing case and new queries that were provided by the
visitors and provides solution or recommendation by taking best cases to the
new query. In this study, JCOLIBRI case base development tool was used to
develop the prototype of case based recommender system. 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 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 was 80% and 82% by domain experts and visitors respectively. The performance of the system
was also measured using the standard measure of relevance (IR system)
recall, precision and accuracy measures, where the system registered 83%
recall, 61% precision and 85.4% accuracy. |
