Akademik Veri Yönetim Sistemi
Tezin Türü: Yüksek Lisans
Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü, Türkiye
Tezin Onay Tarihi: 2005
Tezin Dili: İngilizce
Öğrenci: Serkan Ulucan
Danışman: AYDAN MÜŞERREF ERKMEN
Özet: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 أcollaborative filteringؤ and أcontent-based filteringؤ. Sometimes these approaches are called as أuser profilingؤ 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 users̕ actions are independent of their instant context (location, time_etc). But as for mobile environment, the users̕ actions are strictly dependent on their instant context. These dependencies give raise to need of filtering items/actions with respect to the users̕ instant context. In this thesis, an approach coupling approaches from two different domains, one is the أmobile environmentؤ and other is the أwebؤ, is proposed. Hence, it will be possible to separate whole approach into two phases: أcontext-aware predictionؤ and أuser profilingؤ. In the first phase, combination of two methods called أfuzzy c-means clusteringؤ and أlearning automataؤ will be used to predict the mobile user̕s 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.