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A cloud-based recommendation service using principle component analysis–scale-invariant feature transform algorithm

  • New Trends in data pre-processing methods for signal and image classification
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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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Authors and Affiliations

  1. Istanbul Kültür University, E5 Karayolu üzeri Bakirköy, 34156, Istanbul, Turkey

    Akhan Akbulut, Cagatay Catal & Fatma Patlar Akbulut

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  1. Akhan Akbulut
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  2. Cagatay Catal
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  3. Fatma Patlar Akbulut
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Correspondence to Akhan Akbulut.

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Akbulut, A., Catal, C. & Akbulut, F.P. A cloud-based recommendation service using principle component analysis–scale-invariant feature transform algorithm. Neural Comput & Applic 28, 2859–2868 (2017). https://doi.org/10.1007/s00521-017-2858-2

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  • DOI: https://doi.org/10.1007/s00521-017-2858-2

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