A Novel Cloud Services Recommendation System Based on Automatic Learning Techniques

Djiroun, Rahma; Guesssoum, Meriem Amel; Boukhalfa, Kamel; Benkhelifa, Elhadj

doi:10.1109/ictcs.2017.58

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…In [12], the authors proposed a strategy for recommending cloud services based on learning and data mining approaches to improve user visibility. In addition, they suggest using this approach to study how various users interact with cloud services (consulting, using, unsubscribing, etc.)…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Approach to Improve Recommendation of Cloud Services for Personalized QoS Requirements

Samadhiya,

2024

Electronics

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Cloud-service recommendation systems make suggestions based on ratings provided by cloud users. These ratings may contain sparse data, which makes it difficult to speculate on suitable cloud services. Moreover, new cloud users often suffer from cold-start difficulties. Therefore, in this study, we attempt to better overcome these two challenges, i.e., cold start and data sparsity, using a hybrid approach incorporating neural matrix factorization, deep autoencoders, and suitable questionnaires. The proposed approach provides a list of the top N cloud service providers for old cloud users based on the predicted preferences using quality of service data and asymmetrically weighted cosine similarity. To address the cold start problem, we design a questionnaire to survey new user preferences and suggest personalized cloud providers accordingly. The experiments based on the Cloud Armor database demonstrate that our approach outperforms other models. The proposed approach has a precision of 85% and achieves a mean absolute error (MAE) of 0.05 and root-mean-square error (RMSE) of 0.14 for the differences between the input and predicted values. We also receive a satisfaction level of nearly 78.5% for recommendation lists provided to new cloud service customers.

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid Approach to Improve Recommendation of Cloud Services for Personalized QoS Requirements

Samadhiya,

2024

Electronics

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“…An RS framework combining content-based and behavior-based recommenders was introduced. 33 A K-means clustering algorithm was used to aggregate past invocation data. 34 The community effect formed between users' trusted friendships has also been used to synthesize the recommender's features.…”

Section: Related Workmentioning

confidence: 99%

An elastic recommender process for cloud service recommendation scalability

Zhou

Wang

et al. 2022

Concurrency and Computation

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Cloud computing services are ubiquitous in society and cloud recommender systems play a crucial role in intelligently selecting services for cloud users. Currently, recommendations are static with low scalability. Only one recommendation list is generated at a time and the recommender strategy in the recommendation cycle is not adjustable. This paper presents a new elastic recommender process (ERP) for cloud users. A Markov model is used to characterize the dynamic relationship between different user states. The ERP generates an elastic recommendation that can be used to dynamically adjust the recommender strategy to meet the user's needs based on their browsing records in the current service cycle without the recommender system's involvement. Experimental results show that the ERP improves the effectiveness of the recommender thus increasing the accuracy and diversity of its recommendations.

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“…In a cloud environment, the resource selection procedure is incredibly difficult and less reliable due to the immense complexity and constraints of existing methodologies [8]. In this context, manual approaches will be inefficient and time-consuming; therefore, automated recommendation systems are required to assist the consumer in choosing the best cloud services [9,10].…”

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confidence: 99%

Cloud Broker Recommendation Framework to Provide Trustworthy Cloud Services to the End User

Marimuthu¹,

Akilandeswari²,

Varasree³

et al. 2022

SCPE

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In recent years, many cloud services have become available on the Website}. Discovering suitable cloud services for the end user is incredibly complex and difficult. The cloud brokerage service is an application that aids in providing solutions for this problem. It recommends suitable cloud service providers to the end users depending on their relevant requirements. The Internet provides access to a wide variety of cloud brokers. As a result, choosing a cloud broker or service provider is both time-consuming and tedious. It is now becoming a necessity to choose a proper cloud brokerage service based on trust. Research works found in the literature address some of the issues and provide feasible solutions by proposing frameworks, optimizations and rule based algorithms. However, those works focus solely on delivering a trustworthy service to the end user through application of techniques and algorithms. There is no proper framework model in place to provide suitable and trustworthy recommended services to the users. This article provides a detailed description of the frameworks that are offered by the researchers, including issues and proposes a trustworthy recommendation framework (TRF) to provide trustworthy services to the end user. This article also presents a Trustworthy Recommended Weighted value (TRWv) approach for determining trustworthy services, and it is discovered that the proposed method achieves high accuracy (91.3%) when compared to similar works.

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A Novel Cloud Services Recommendation System Based on Automatic Learning Techniques

Cited by 9 publications

References 14 publications

Hybrid Approach to Improve Recommendation of Cloud Services for Personalized QoS Requirements

Hybrid Approach to Improve Recommendation of Cloud Services for Personalized QoS Requirements

An elastic recommender process for cloud service recommendation scalability

Cloud Broker Recommendation Framework to Provide Trustworthy Cloud Services to the End User

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