Cloud Service Recommendation Research Articles

Cloud Service Recommendation with Bi-LSTM and GLDA Based Approach

With the rapid growth of cloud computing and the increasing number of cloud services available, the accurately selecting and recommending cloud service has become a challenging task for users. Traditional methods based on text classification and topic modeling have limitations in handling the diverse range of cloud services. To overcome these challenges, this paper proposes a novel approach that combines Gaussian Latent Dirichlet Allocation (GLDA) and attention mechanisms to enhance the accuracy and effectiveness of cloud service’s selection and recommendation. GLDA captures the continuity and relevance between words, improving topic modeling. The attention mechanisms focus on relevant tag fragments to guide attention toward content related to cloud services. By combining GLDA and attention mechanisms, our method offers advantages in semantic information and precise recommendations. This paper provides a promising solution to improve the selection and recommendation of cloud services, offering users a better experience.

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

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.

Manufacturing cloud service recommendation model based on deep feature learning and user preference perception

ABSTRACT The manufacturing cloud service recommendation model is a key technology for users to quickly discover and obtain the personalized services they need from the increasingly expanding manufacturing cloud; however, the manufacturing cloud service recommendation model faces problems, such as difficulty in representing deep features of services and too sparse user preference models. To address the above problems, this paper proposes a manufacturing cloud service recommendation model framework based on deep feature learning and user preference perception and investigates the key technologies involved. On the one hand, a Convolutional Neural Network (CNN)-based deep feature extraction method for manufacturing cloud services is proposed by combining the Latent Dirichlet Allocation (LDA) topic model and word2vec. On the other hand, the concept of a category is introduced through the method of label clustering to perceive user behavior, efficiently build user preference models, and solve the sparse problem of the user-service matrix. Based on this, the execution process of the recommendation model for manufacturing cloud services is analyzed by fusing the deep features of services and user preference models. The experimental results on the actual dataset prove that the method greatly improves the recommendation effect.

Retraction Note to: Cloud service recommendation system based on clustering trust measures in multi-cloud environment

Retraction Note to: Cloud service recommendation system based on clustering trust measures in multi-cloud environment

An elastic recommender process for cloud service recommendation scalability

AbstractCloud 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.

An Approach to Churn Prediction for Cloud Services Recommendation and User Retention

The digital world is very dynamic. The ability to timely identify possible vendor migration trends or customer loss risks is very important in cloud-based services. This work describes a churn risk prediction system and how it can be applied to guide cloud service providers for recommending adjustments in the service subscription level, both to promote rational resource consumption and to avoid CSP customer loss. A training dataset was built from real data about the customer, the subscribed service and its usage history, and it was used in a supervised machine-learning approach for prediction. Classification models were built and evaluated based on multilayer neural networks, AdaBoost and random forest algorithms. From the experiments with our dataset, the best results for a churn prediction were obtained with a random forest-based model, with 64 estimators, having 0.988 accuracy and 0.997 AUC value.

Cloud service recommendation for small and medium-sized enterprises: A context-aware group decision making approach

Recently, the enormous advantages of cloud services make them increasingly appealing to the small and medium-sized enterprises. The growing number of available services makes it challenging to select trustworthy services. Existing approaches focus on user preferences to guide personalized services recommendation for individual users, but lack of the research on trustworthy service recommendation for the small and medium-sized enterprises that represents a group user consisting of multiple individual users. For this type of enterprise, the cloud services recommendation must address the challenges from the diverse client context of individual users, the imprecise quality of experience in an uncertain cloud environment and the invalid or unsatisfactory recommendations. A client context-aware approach is proposed to recommend trustworthy cloud services for the small and medium-sized enterprises based on non-compensatory multi-criteria decision-making. In it, a type of client context is viewed as an independent evaluation criterion, and the interval neutrosophic numbers are employed to measure the fuzzy trustworthiness of cloud services. Based on the investigated outranking relations of interval neutrosophic numbers, a non-compensatory multi-criteria decision-making procedure via an improved ELECTRE III method is developed to rank candidate services. Experimental results demonstrate that this approach could efficiently produces the accurate ranking results of cloud services and effectively recommend the trustworthy service for small and medium-sized enterprises.

A cloud service recommendation method based on extended multi‐source information fusion

AbstractWith the rapid development of information technology, the problem of “information overload” emerges when users choose cloud services. How to integrate multi‐source information to achieve accurate service recommendation is an urgent problem to be solved by current recommendation systems. This article proposes a cloud service recommendation method based on extended multi‐source information fusion. First, we propose a score prediction based on matrix decomposition and topic matrix, and we fully mine existing explicit data and feedback data, such as user ratings, social trust information, reviews, and user personalized preferences and so on. Second, in order to solve the problems of data sparseness and cold start of the system, we integrate the score, social trust information and review into a comprehensive model through collaborative filtering (CF), and propose a multi‐source information fusion recommendation method. The CF fusion method mainly combines two parts: social matrix decomposition and topic matrix decomposition. Finally, in order to further improve the accuracy and scalability, the implicit feature matrix is integrated into the user rating matrix, and the original CF enhancement based on scoring matrix decomposition is a matrix decomposition method that can learn implicit features. Experimental results show that compared with other recommendation algorithms, the cloud service recommendation method proposed in this article can improve the recommendation accuracy and allow users to choose satisfactory cloud services.

Cloud Service Recommendation Using Trust Data and Reliability Measurements

Cloud Service Recommendation Using Trust Data and Reliability Measurements

Energy-Efficient Cloud Service Selection and Recommendation Based on QoS for Sustainable Smart Cities

The growing demand for cloud technology brings several cloud service providers and their diverse list of services in the market, putting a challenge for the user to select the best service from the inventory of available services. Therefore, a system that understands the user requirements and finds a suitable service according to user-customized requirements is a challenge. In this paper, we propose a new cloud service selection and recommendation system (CS-SR) for finding the optimal service by considering the user’s customized requirements. In addition, the service selection and recommendation system will consider both quantitative and qualitative quality of service (QoS) attributes in service selection. The comparison is made between proposed CS-SR with three existing approaches analytical hierarchy process (A.H.P.), efficient non-dominated sorting-sequential search (ENS-SS), and best-worst method (B.W.M.) shows that CR-SR outperforms the above approaches in two ways (i) reduce the total execution time and (ii) energy consumption to find the best service for the user. The proposed cloud service selection mechanism facilitates reduced energy consumption at cloud servers, thereby reducing the overall heat emission from a cloud data center.

Ensemble deep neural network based quality of service prediction for cloud service recommendation

Applications of Cloud Services are increasing day by day, and so is the difficulty of choosing the best-suited service for a customer. Quality of Service (QoS) parameters can be used for quality assurance and evaluation; further, a service can be recommended based on these QoS parameters’ values. Recommendation systems are getting much attention lately. It has a crucial role in almost all the major commercial platforms and many improvements are being made to make the recommendations more precise and closer to the user’s requirements. Conventional Machine Learning algorithms and statistical analysis methods, presently are not that efficient in learning the complex correlation between data elements. Lately, Deep Learning models have proven to be practical and precise in areas like natural language processing, image processing, data mining, & data interpretation. However, there are not many examples of complete Deep Learning applications for cloud service recommendation systems, though some works partially use Deep Learning. We propose the Ensemble of Deep Neural Networks (EDNN) method, which is of the hybrid type, i.e., the fusion of neighborhood-based and neural network model-based methods. The output obtained from both the models are combined using another different neural network model. Our approach for predicting QoS values is simple and different from previous works, and the results show that it outperforms other classical methods marginally.

SaaSRec+: a new context-aware recommendation method for SaaS services.

Cloud computing is an attractive model that provides users with a variety of services. Thus, the number of cloud services on the market is growing rapidly. Therefore, choosing the proper cloud service is an important challenge. Another major challenge is the availability of diverse cloud services with similar performance, which makes it difficult for users to choose the cloud service that suits their needs. Therefore, the existing service selection approaches is not able to solve the problem, and cloud service recommendation has become an essential and important need. In this paper, we present a new way for context-aware cloud service recommendation. Our proposed method seeks to solve the weakness in user clustering, which itself is due to reasons such as 1) lack of full use of contextual information such as cloud service placement, and 2) inaccurate method of determining the similarity of two vectors. The evaluation conducted by the WSDream dataset indicates a reduction in the cloud service recommendation process error rate. The volume of data used in the evaluation of this paper is 5 times that of the basic method. Also, according to the T-test, the service recommendation performance in the proposed method is significant.

RETRACTED ARTICLE: Cloud service recommendation system based on clustering trust measures in multi-cloud environment

Due to technological advancement, cloud computing is an inevitable form of computing these days and is considered a boon to mid-scale industries. As the usage of cloud computing increases day-by-day, the service deployment improves every single day, which paves the way for security threats as well. Finding trustworthy service is a highly challenging problem, which may lead to time consumption or end with inappropriate services. Due to this problem, end user needs trust based appropriate service with minimum time consumption and the service should be reliable too. Hence, a cloud service recommendation system is the current need of the cloud environment. From a pool of available cloud services, the proposed system can recommend the time conserving reliable trustworthy services. This work attempts to keep this as the goal and presents a cloud service recommendation system using clustering based trust degree computation algorithm. Trust measures are deliberating to compute the trust degree (TD) for each dynamic service, which is computed for every time and the historical information is maintained as well. Since the trust agent clustered the services in automated fashion, to isolates the most trustworthy services from all the available clustered cloud services and efficiently allocates services to the end user using trustworthy service allocation algorithm. Process of service search and recommendation needs minimum time consumption. Registering service with trust agent (TA) provides most reliable trust worthy services. The performance of this recommendation system is evaluated in terms of precision, recall, F-measure and time consumption rates. The average F-measure rate of the proposed work is computed by varying the count of users from 200 to 300 and the average F-measure rate is 91.85% with minimal time consumption than the existing approaches.

Improved quality of service-based cloud service ranking and recommendation model

One of the ongoing technologies which are used by large number of companies and users is cloud computing environment. This computing technology has proved that it provides certainly a different level of efficiency, security, privacy, flexibility and availability to its users. Cloud computing delivers on demand services to the users by using various service-based models. All these models work on utility-based computing such that users pay for their used services. Along with the various advantages of the cloud computing environment, it has its own limitations and problems such as efficient resource identification or discovery, security, task scheduling, compliance and sustainability. Among these resource identification and scheduling plays an important role because users always submits their jobs and expects responses in least possible time. Research is happening all around the world to optimize the response time, make span so as to reduce the burden on the cloud resources. In this paper, QoS based service ranking model is proposed for cloud computing environment to find the essential top ranked services. Proposed model is implemented in two phases. In the first phase, similarity computation between the users and their services is considered. In the second phase, computing the missing values based on the computed similarity measures is calculated. The efficiency of the proposed ranking is measured and the average precision correlation of the proposed ranking measure is showing better results than the existing measures.

CloudFNF: An ontology structure for functional and non-functional features of cloud services

Recently, cloud computing becomes one of the main orientations of many researchers and companies in the IT area. Therefore, a huge number of cloud services have been developed. Because of the diversity and heterogeneity in providing these services, it is urgently needed to develop a unified cloud ontology. Such ontology can classify these services appropriately and participate as a mapping layer to present such services in a unified description format. Although many studies have been conducted to build cloud ontologies, they have adopted the cloud service layers-based structure. On the other hand, the existing cloud services may involve functionalities from different layers due to the continual increase in the complexity of customer demands. Unfortunately, there are no clear relations to organize the interventions among these layers. Therefore, such services may be difficult to be classified into a specific cloud service layer. Additionally, the layers-based structure of ontologies represents an obstacle to address important issues, such as cloud service recommendation. Despite there are few cloud service functionality-based cloud ontologies, they suffer from many overlaps, lack of semantic relations, or poor granularity of concepts. Also, all the existing cloud ontologies (i.e., layers-based and functionality-based) lack important criteria, such as completeness, consistency, conciseness, clarity, preciseness, and granularity. In this paper, a comprehensive cloud ontology called CloudFNF has been proposed to overcome such drawbacks. According to the structure of the proposed ontology, cloud services are classified as functionality-based instead of layers-based. Also, non-functional features of cloud services (i.e., configuration and QoS features) are considered to enable services of the same functionalities to be ranked efficiently. Based on our previously suggested cloud ontology evaluation framework, our proposed cloud ontology has been evaluated compared to the most related cloud ontologies. The evaluation results show that the proposed CloudFNF ontology outperforms the other ontologies.

Service Association Factor (SAF) for Cloud Service Selection and Recommendation

Cloud computing is one of the leading technology in IT and computer science domain. Business IT infrastructures are equipping themselves with modern regime of clouds. In the presence of several opportunities, selection criteria decision becomes vital when there is no supporting information available. Global clouds also need evaluation and assessment from its users that what they think about and how new ones could make their selection as per their needs. Recommended systems were built to propose best services using customer's feedback, applying quality of service parameters, assigning scores, trust worthiness and clustering in different forms and models. These techniques did not record and use interrelationships between the services that is true impact of service utilization. In the proposed approach, service association factor calculates value of interrelations among services used by the end user. An intelligent leaning based recommendation system is developed for assisting users to select services on their respective preferences. This technique is evaluated on leading service providers and results show that learning base system performs well on all types of cloud models.

Comparative study of Topk based on Fagins algorithm using correlation metrics in cloud computing QoS

With the exponential growth of cloud computing services recently, several internet technologies began to require the processing of multi-criteria ranking. The collaborative filtering methods and Topk selection computations have been proven to be more effective in information retrieval. In addition, they are widely used to evaluate the QoS for cloud services recommendation. However, the biggest challenge is not only to reduce the size of skyline results, but also to have a good response quality that reflects the user requirement. To deal with these problems, we propose in this paper an approach based on Topk algorithm combined with the weighted sum method. This approach is introduced for refining the skyline result using the Topk query advantages. Then in order to evaluate the performance of our approach, we compared the proposed algorithm with Fagin's one. The experimental results show the efficiency of our algorithm particularly in comparing the runtime results and using specific metrics of correlation.

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

With the growing popularity of cloud manufacturing (CMfg), an increasing number of functionally equivalent cloud services are available on the Internet, which makes cloud service recommendation an important topic recently. Since the quality-of-service (QoS) varies largely in cloud environment, many Collaborative Filtering (CF) approaches are recently proposed to predict the uncertain QoS by utilizing the historical QoS records of similar users. In addition to the data sparsity problem, however, most existing approaches also ignore the task similarity among different users, while dissimilar tasks requested by similar users may lead to quite different QoS prediction results, and thus cannot achieve higher prediction accuracy and recommendation quality. To address this problem, we develop a novel clustering-based and trust-aware approach. Firstly, a set of similar users can be identified from the view of task similarity using clustering-based algorithm, where task similarity can be computed by incorporating both explicit textual information and rating information as well as implicit context information. Secondly, considering the fact that the QoS values may be contributed by unreliable users, we design a trust-aware CF approach by merging local and global trust values, to reconstruct trust network of the clustered users. Finally, we combine the clustering-based algorithm and trust-aware approach to make a more personalized QoS prediction and reliable cloud service recommendation for the active user in CMfg. Experimental results on two real-world data sets demonstrate the effectiveness of the proposed approach compared with other state-of-the-art approaches.

Cloud service recommendation based on unstructured textual information

With the rapid growth of cloud service platform, it is more and more difficult for users to discover cloud services satisfying their personalized demands. The unstructured textual information, such as the service descriptive texts and service tags, contain rich features of cloud services and are useful for cloud service platforms to construct personalized service recommendation. This paper proposes a two-stage model for cloud service recommendation by integrating the information of service descriptive texts and service tags. In the first stage, we propose a Hierarchical Dirichlet Processes (HDP) model to cluster cloud services into an optimal number of clusters based on descriptive texts. In the second stage, we propose a Personalized PageRank algorithm based on service tags to rank and recommend cloud services in each cluster. Our experiments on a real data set show that the proposed two-stage model can segment cloud services well and obtain accurate recommendation results compared with the baseline methods.

A cloud services recommendation system based on Fuzzy Formal Concept Analysis

Cloud computing is an attractive paradigm which offers variant services on demand. Many available cloud services offer the same or similar functionalities, which made it challenging for cloud users to choose a suitable service that meets with their preferences. Existing service selection approaches were not enough to solve this challenge. That's why researchers went for recommendation approaches trying to find a solution. Cloud service recommendation has become an important technique for cloud services. It helps users decide whether a service satisfies their requirements or not. However, two main recommendation problems remain unsolved yet, data sparsity and cold start. In addition, existing solutions mostly tried to adapt techniques inherited from Web service and e-commerce domains. This approach is not always adequate due to many reasons such as the cloud architecture, the various service models, etc. To address the problems stated above, we propose a Collaborative Filtering based recommendation system for cloud services using Fuzzy Formal Concept Analysis (Fuzzy FCA). Fuzzy FCA has a solid mathematical foundation and it's based on the lattice theory. The lattice representation will give an explicit description of our cloud environment (users, services, ratings, etc.) and, then, extract the pertinent information from it (similar users to an active user, ratings of each similar user, top services, etc.) which will make the recommendations more suitable. Experimental results confirmed our expectations and proved the efficiency of such an approach.