Author/Authors :
utku, anıl gazi üniversitesi - mmf - bilgisayar mühendisliği bölümü, ANKAR, turkey , akcayol, muhammet ali gazi üniversitesi - mmf - bilgisayar mühendisliği bölümü, ANKAR, turkey
Title Of Article :
A Comparative and Comprehensive Review for Learning and Adaptive Recommendation Systems
Abstract :
Due to the dynamic and heterogeneous nature of Web, it becomes increasingly difficult for users to choose between large amounts of data. For this reason, modeling of users and accessing customized information are becoming important. Recommendation systems aiming provide most appropriate and efficient services by offering customized recommendations to users. Traditional recommendation systems offer users static suggestions and do not include time-varying users’ preferences in their suggestion strategies. In this study, a comprehensive investigation and comparison of the recommendation systems, which adaptively create proposals for changing user preferences and learn users’ preferences, has been presented.
NaturalLanguageKeyword :
Adaptive Clustering , Personalized Recommendations , Learnable Recommender Systems , Recommendation Systems
JournalTitle :
Erciyes University Journal Of The Institute Of Science and Technology
