Publication: A cloud-based recommendation service using principle component analysis–scale-invariant feature transform algorithm
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Date
2017-10
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Abstract
Cloud computing delivers resources such as
software, data, storage and servers over the Internet; its
adaptable infrastructure facilitates on-demand access of
computational resources. There are many benefits of cloud
computing such as being scalable, paying only for consumption, improving accessibility, limiting investment
costs and being environmentally friendly. Thus, many
organizations have already started applying this technology
to improve organizational efficiency. In this study, we
developed a cloud-based book recommendation service
that uses a principle component analysis–scale-invariant
feature transform (PCA-SIFT) feature detector algorithm to
recommend book(s) based on a user-uploaded image of a
book or collection of books. The high dimensionality of the
image is reduced with the help of a principle component
analysis (PCA) pre-processing technique. When the mobile
application user takes a picture of a book or a collection of
books, the system recognizes the image(s) and recommends similar books. The computational task is performed
via the cloud infrastructure. Experimental results show the
PCA-SIFT-based cloud recommendation service is
promising; additionally, the application responds faster
when the pre-processing technique is integrated. The proposed generic cloud-based recommendation system is
flexible and highly adaptable to new environments.
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Keywords
Özellik Seçimi, Özellik Çıkarma, Nesne Tanıma, Bulut Bilişim, Öneri Sistemi, Feature Selection, Feature Extraction, Object Recognition, Cloud Computing, Recommendation System
Citation
10