New prediction methods for collaborative filtering
Küçük Resim Yok
Tarih
2016
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pamukkale Univ
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Companies, in particular e-commerce companies, aims to increase customer satisfaction, hence in turn increase their profits, using recommender systems. Recommender Systems are widely used nowadays and they provide strategic advantages to the companies that use them. These systems consist of different stages. In the first stage, the similarities between the active user and other users are computed using the user-product ratings matrix. Then, the neighbors of the active user are found from these similarities. In prediction calculation stage, the similarities computed at the first stage are used to generate the weight vector of the closer neighbors. Neighbors affect the prediction value by the corresponding value of the weight vector. In this study, we developed two new methods for the prediction calculation stage which is the last stage of collaborative filtering. The performance of these methods are measured with evaluation metrics used in the literature and compared with other studies in this field.
Açıklama
Anahtar Kelimeler
Recommender system, Collaborative filtering, Prediction methods
Kaynak
Pamukkale University Journal of Engineering Sciences-Pamukkale Universitesi Muhendislik Bilimleri Dergisi
WoS Q Değeri
N/A
Scopus Q Değeri
Cilt
22
Sayı
2
