Up to now various recommendation systems have been proposed for web based applications such as e-commerce and information retrieval where a large amount of product or information is available. Basically, the task of the recommendation systems in those applications, for example the e-commerce, is to find and recommend the most
relevant items to users/customers. In this domain, the most prominent approaches are collaborative filtering and content-based filtering. Sometimes these approaches are called as user profiling as well.
In this work, a context-aware recommendation system is proposed for mobile environment, which also can be considered as an extension of those recommendation
systems proposed for web-based information retrieval and e-commerce applications. In the web-based information retrieval and e-commerce applications, for example in an
online book store (e-commerce), the users& / #8217 / actions are independent of their instant context (location, time& / #8230 / etc). But as for mobile environment, the users& / #8217 / actions are strictly dependent on their instant context. These dependencies give raise to need of filtering items/actions with respect to the users& / #8217 / instant context.
In this thesis, an approach coupling approaches from two different domains, one is the mobile environment and other is the web, is proposed. Hence, it will be possible to
separate whole approach into two phases: context-aware prediction and user profiling. In the first phase, combination of two methods called fuzzy c-means
clustering and learning automata will be used to predict the mobile user& / #8217 / s motions in context space beforehand. This provides elimination of a large amount of items placed in
the context space. In the second phase, hierarchical fuzzy clustering for users profiling will be used to determine the best recommendation among the remaining items.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12606845/index.pdf |
| Date | 01 December 2005 |
| Creators | Ulucan, Serkan |
| 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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