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İşbirlikçi filtreleme için yeni tahminleme yöntemleri

New prediction methods for collaborative filtering

Journal Name:

  • Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi

Publication Year:

  • 2016
DOI: 
10.5505/pajes.2014.44227

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
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.
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Abstract (Original Language): 
Firmalar, özellikle e-ticaret firmaları, öneri sistemleri kullanarak müşteri memnuniyetini, dolayısı ile karlılıklarını artırmayı hedeflemektedirler. Günümüzde Öneri Sistemleri yaygın olarak kullanılmakta ve bunları kullanan firmalara stratejik avantajlar sağlamaktadır. Bu sistemler farklı aşamalardan oluşurlar. İlk aşamada kullanıcı-ürün değerlendirme matrisi kullanılarak aktif kullanıcı ile diğer kullanıcılar arasındaki benzerlikler bulunur. Daha sonra bu benzerliklerden yola çıkılarak aktif kullanıcının yakın komşuları belirlenir. Tahmin hesaplama aşamasında, ilk adımda bulunan benzerlikler kullanılarak aktif kullanıcının yakın komşularının ağırlık vektörü oluşturulur. Komşular tahmin hesaplamasına bu ağırlıklar oranında etki ederler. Bu çalışmamızda işbirlikçi filtreleme algoritmalarının son basamağı olan tahmin hesaplama adımı için yeni yöntemler geliştirilmiştir. Bu yöntemlerin başarımı literatürde kullanılan değerlendirme metrikleri ile ölçülüp bu alanda yapılan çalışmalar ile karşılaştırılmıştır.
FULL TEXT (PDF): 
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  • 2
123
128
  • Turkish

REFERENCES

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Pamukkale Univ Muh Bilim Derg, 22(2), 123-128, 2016
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