The popularity of e-commerce sites and applications that use recommendations and
user modeling is increased recently. The development and contest in tourism calls
attention of large-scale IT companies. These companies have started to work on
recommendation systems and user modeling on tourism sector. Some of the
clustering methodologies, neighboring methods and machine learning algorithms are
commenced to use for making predictions about tourist&rsquo / s interests while he/she is
traveling around the city. Recommendation ability is the most interesting thing for a
tourist guide application. Recommender systems are composed of two main
approaches, collaborative and content-based filtering. Collaborative filtering
algorithms look for people that have similar interests and properties, while contentbased
filtering methods pay attention to sole user&rsquo / s interests and properties to make
recommendations. Both of the approaches have advantages and disadvantages, for
that reason sometimes these two approaches are used together. Chosen method
directly affects the recommendation quality, so advantages and disadvantages of both
methods will be examined carefully.
Recommendation of locations or services can be seen as a classification problem.
Artificial intelligent systems like neural networks, genetic algorithms, particle swarm
optimization algorithms, artificial immune systems are inspired from natural life and
can be used as classifier systems. Artificial immune system, inspired from human
immune system, has ability to classify huge numbers of different patterns. In this
paper ESGuide, a tourist guide application that uses artificial immune system is
examined. ESGuide application is a client-server application that helps tourists while
they are traveling around the city. ESGuide has two components: Map agent and
recommender agent. Map agent helps the tourist while he/she interacts with the city
map. Tourist should rate the locations and items while traveling. Due to these ratings
and client-server interaction, recommender agent tries to predict user interested
places and items. Tourist has a chance to state if he/she likes the recommendation or
not. If the tourist does not like the recommendation, new recommendation set is
created and presented to the user.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12606852/index.pdf |
| Date | 01 December 2005 |
| Creators | Alkilicgil, Erdem |
| Contributors | Erkmen, Aydan |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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