A personalized clustering-based and reliable trust-aware QoS prediction approach for cloud service recommendation in cloud manufacturing

Liu, Jian; Chen, You-Ling

doi:10.1016/j.knosys.2019.02.032

Cited by 89 publications

(41 citation statements)

References 36 publications

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“…The similarity degree is calculated by summing the basic similarities in function, input and output, and other non-functional attributes [9]. Liu and Chen (2019) proposed an approach to recommending cloud manufacturing services by clustering and similarity-based recommending. The similarity degree takes into account both task similarity and QoS similarity [10].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Liu and Chen (2019) proposed an approach to recommending cloud manufacturing services by clustering and similarity-based recommending. The similarity degree takes into account both task similarity and QoS similarity [10].…”

Section: Literature Reviewmentioning

confidence: 99%

“…EOT jk x ijk ∀i (10) where SOT i jk and EOT jk are the start time and end time of a time window by cloud service MS jk .…”

Section: Constraintsmentioning

confidence: 99%

“…Service costs and transportation cost per unit obey uniform distributions U[50, 1000] and U[0.005, 0.02], respectively. Carbon emissions and water usage are subject to normal distribution N [10,48] and N[0.5, 6.5], respectively. For each case, 10 experiments are randomly generated and the average coverage ratio and computational times are derived based on the 10 experiments.…”

Section: Comparison With the Weighted Sum Approachmentioning

confidence: 99%

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Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability

Yang

Qi-dong

et al. 2020

Sustainability

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Cloud manufacturing is an emerging service-oriented paradigm that works by taking advantage of distributed manufacturing resources and capabilities to collaboratively perform a manufacturing task, with the consideration of QoS (Quality of Service) requirements such as cost, time and quality. Incorporating environmental concerns and sustainability into cloud manufacturing to produce a much greener product has become an urgent issue since there is fierce market competition and an increasing environment consciousness from customers. In this paper, we present a multi-objective optimization approach to selecting and scheduling cloud manufacturing services from the viewpoints of the economy and environment including carbon emissions and water resource. Subject to the carbon cap regulation, a multi-objective model for a cloud manufacturing task is built with the aim of minimizing total costs, carbon emissions, and water resource use. Transportation mode selections and carbon emissions from both cloud manufacturing services and transportation activities are taken into account in this model. The ε-constraint method is employed to obtain the exact Pareto front of optimal solutions. A case study from automobile cloud manufacturing is used to illustrate the effectiveness of the presented approach. Numerical experiments are conducted to compare the presented approach and the simple additive weighting method. The results show that the presented ε-constraint method can obtain a better and more diverse Pareto set of solutions and that it can solve the models in a reasonable time.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…EOT jk x ijk ∀i (10) where SOT i jk and EOT jk are the start time and end time of a time window by cloud service MS jk .…”

Section: Constraintsmentioning

confidence: 99%

Section: Comparison With the Weighted Sum Approachmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability

Yang

Qi-dong

et al. 2020

Sustainability

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“…Aiming at the data sparsity problem occurring in the early stage, the trust relationship of user subjectivity was introduced as a constraint, which meant that the degree of trust was introduced into the calculation of the user similarity and the results of similar calculations and predictions were constrained. In documents [19]- [20], considering the potential interest preferences between new users and new projects, a clustering method was used to obtain the nearest neighbor set of new users and the projects of the nearest neighbor set were recommended to new users. In document [21], to solve the cold start problem, the interaction information between the attribute preference information of the project and the user's rating behavior was not fully utilized.…”

Section: Related Workmentioning

confidence: 99%

Personalized Product Service Scheme Recommendation Based on Trust and Cloud Model

Wang

et al. 2020

IEEE Access

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Considering the low satisfaction rate and low efficiency of product service plans, a personalized product service plan recommendation method adopting the degree of trust and cloud model is proposed. The recommendation algorithm mainly includes calculating the similarity and the prediction results of the scheme. First, by fully considering the user's subjective characteristics, the user's trust is used to improve the traditional similarity. Second, considering data sparsity and discreteness, the cloud drop distance similarity calculation method is introduced in the process of calculating the trust similarity, and a new similarity is generated via the weighted synthesis to predict and fill in the gaps in the data. Then, when the new user does not have the cold start problem caused by the historical score record, the neural network method can be used to classify the users based on the user characteristics. The method is proposed and introduced to predict sparse user interest features and obtain similar user sets based on feature classification. The corresponding program offers recommendations. Finally, the effectiveness and rationality of the proposed method are verified by using the recommendations for a machine tool product for a manufacturing enterprise as an example. INDEX TERMS Sparsity, cold start, user trust, cloud drop distance similarity.

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Web service quality of service prediction via regional reputation‐based matrix factorization

Ghafouri

Hashemi

Razzazi

et al. 2021

Concurrency and Computation

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Quality of Service (QoS) of Web services plays an essential role in selecting Web services by consumers. The dynamic QoS attributes of Web services have different values for different users. Therefore, the value of many Web services' QoS features for many users are undetermined, and these values should be predicted. The collaborative filtering (CF) method is one of the most successful approaches to predict these values. CF‐based methods use the QoS values contributed by the other users for prediction and, consequently, the values contributed by unreliable users can decrease the accuracy of prediction. To utilize the reputation of users can be regarded as one of the conventional approaches to overcome this problem. In this paper, we have defined a concept called regional reputation that represents the reputation of a user for users in each geographical region. Regional reputation has been achieved with the combination of the location information of the users and their reputation. Subsequently, by combining this concept with the matrix factorization, we have proposed a prediction method called regional reputation‐based matrix factorization. This approach has been able to improve the accuracy of prediction and be more persistent to the data contributed by unreliable users.

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A personalized clustering-based and reliable trust-aware QoS prediction approach for cloud service recommendation in cloud manufacturing

Cited by 89 publications

References 36 publications

Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability

Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability

Personalized Product Service Scheme Recommendation Based on Trust and Cloud Model

Web service quality of service prediction via regional reputation‐based matrix factorization

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