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Improved clustering-based hybrid recommendation system to offer personalized cloud services

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A Correction to this article was published on 11 October 2023

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Abstract

The ever-increasing number of cloud services has led to the service’s identification problem. It has become difficult to provide users with cloud services that meet their functional and non-functional requirements, especially as many cloud services offer the same or similar functionality but with different execution constraints (cloud characteristics, QoS, price, and so on). Service recommendation systems can solve the service’s identification problem by helping users to retrieve the right cloud services according to their desired needs. However, the majority of service recommendation systems rely on user feedback to locate the user’s neighbors, predict missing ratings, and rank the recommended services. As a result, users’ rating histories might cause three major problems: cold start, data sparsity, and malicious attack. In order to deal with these issues, we propose in this paper a hybrid recommendation approach, called “HRPCS”, that provides a list of personalized cloud services to the active user. This approach is based on user and service clustering. In this approach, cloud services are recommended based on the user’s needs (functional and non-functional) and QoS preferences. Then, the services are ranked according to their prices and credibility. Further, the proposed approach returns a list of diversified cloud services. The experimental results confirmed our expectations and proved the effectiveness of our approach.

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Notes

  1. https://www.maxmind.com.

  2. https://developers.google.com/maps/documentation/geocoding/overview

  3. https://wsdream.github.io/

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Authors and Affiliations

  1. Higher Institute of Computer Science and Communication Technologies of Hammam Sousse, University of Sousse, Sousse, Tunisia

    Hajer Nabli & Raoudha Ben Djemaa

  2. National School of Electronics and Telecommunications of Sfax, University of Sfax, Sfax, Tunisia

    Ikram Amous Ben Amor

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  1. Hajer Nabli
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  2. Raoudha Ben Djemaa
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  3. Ikram Amous Ben Amor
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All authors reviewed the manuscript.

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Correspondence to Hajer Nabli.

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The original online version of this article was revised: Under the heading '3.2.3.1 Construction of the service-quality matrix', in the second paragraph, the last sentence was inadvertently overlapped. The sentence has now been corrected. The heading 'Algorithm 1 Functional and non-functional filtering phase' should have been 'Algorithm 2 Functional and non-functional filtering phase'.

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Nabli, H., Ben Djemaa, R. & Amous Ben Amor, I. Improved clustering-based hybrid recommendation system to offer personalized cloud services. Cluster Comput 27, 2845–2874 (2024). https://doi.org/10.1007/s10586-023-04119-2

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