Alan Modelinin Politika Kullanılarak Kişiselleştirilmesi

Günümüzde, çevrimiçi olarak erişilen bilgi üstsel olarak artmaktadır. Bilgi miktarındaki bu artış, kullanıcıların tercihleri ile uyumlu bilgiye etkin bir şekilde erişmesini zorlaştırmaktadır. Bu zorluklar, kişiselleştirme yaklaşımı kullanılarak, bireye özel bilginin ya da servisin sunulması ile aşılabilir. Kişiselleştirme yaklaşımının temeli, kişiye özel bilgiyi temsil eden profil yapısıdır. Kullanıcı-uyarlanabilir bir sistemde, profil tipine özgü bir aramanın sonucunda, kullanıcı ihtiyaçlarına göre kişiselleştirilmiş sonuçlara ulaşılacaktır. Bu çalışmada, kişisel içeriğin sunulması amacı ile anlamsal olarak zengin profiller oluşturulmakta ve geliştirilen kullanıcı profilleri, politikalar ile entegre edilerek kural-tabanlı bir kişiselleştirme sağlanmaktadır. Böylelikle, profili temel alan kısıtlar yardımı ile kişiselleştirilmiş bilgiye erişim etkin bir şekilde gerçekleştirilecektir.

Personalizing Domain Model by Using Policy

Nowadays, the information that can be accessed online is increasing exponentially. However, this increase in the amount of information brings difficulties to users to access information relevant with their preferences in an effective way. These difficulties could be overcomed with providing customized information or service to an individual by using personalization approach. Profiling is the basis of personalization approach and the representation of person specific information. In a user-adaptive system, personalized results will be reached according to the user’s needs after a profile specific search. In this work, semantically rich profiles are created to present personal context and developed user profiles are integrated with policies to provide a rule-based personalization. Thus, personalized information will be achieved in an effective way through profile based constraints.
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