Enhanced similarity measure for personalized cloud services recommendation

Afify, Yasmine M.; Moawad, Ibrahim F.; Badr, Nagwa L.; Tolba, Mohamed F.

doi:10.1002/cpe.4020

Cited by 12 publications

(3 citation statements)

References 36 publications

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“…Considering the frequent changes in cloud service quality over time, Ding et al [8] proposed a time-aware cloud service recommendation method using similarity-enhanced collaborative filtering and the autoregressive integrated moving average model to obtain the time feature of the user similarity, address the data sparsity, and predict the QoS values. To improve the prediction accuracy and recommendation efficiency, Afify et al [9] proposed a hybrid collaborative filtering method based on the model and memory that uses user statistics, service ratings, and user interests to measure the similarity. Liu et al [10] proposed a web recommendation method based on the locations of users and services to improve the prediction accuracy and computational efficiency.…”

Section: Collaborative Filtering Methodsmentioning

confidence: 99%

“…e core idea of service recommendation technology is to identify and predict the needs of users and provide corresponding recommendations to users. Existing popular recommendation methods are roughly divided into several categories: content-based recommendation [2,3], collaborative filtering recommendation [4][5][6][7][8][9][10][11][12], recommendation based on association rules [13][14][15], knowledgebased recommendation [16][17][18], and hybrid recommendation methods [19,20]. Content-based methods recommend similar items to a user according to the items that the user has liked in the past.…”

Section: Introductionmentioning

confidence: 99%

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A Service Recommendation Method Based on Requirements for the Cloud Environment

Guo

Luan

Zheng

et al. 2021

Journal of Control Science and Engineering

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In the cloud computing environment, there are huge amounts of functionally similar cloud services. Additionally, user requirements can change. Therefore, it is difficult to recommend services that meet users’ requirements. To overcome the problems, a service recommendation method based on requirements is proposed. First, we form user communities by clustering to reduce the recommended range. Second, we use the reported QoS (Quality of Service) values and the evaluated QoS values to predict the QoS requirements of users. Third, based on the requirements, the matching degree of users to services is obtained. Finally, based on the similarity between the target user and the user’s neighbors and the difference in their matching degree of service and the ratings of services by the neighbors, we can obtain a list of service recommendations for the target user. Compared to the traditional collaborative filtering method and the deviation-based method, our method improves the recommendation accuracy without lowering the efficiency.

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Section: Collaborative Filtering Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Service Recommendation Method Based on Requirements for the Cloud Environment

Guo

Luan

Zheng

et al. 2021

Journal of Control Science and Engineering

View full text Add to dashboard Cite

show abstract

“…The current recommendation algorithms based on social networks mainly include: graph model-based, 10,11 matrix factorization-based, 12,13 and probability model-based [14][15][16] methods. Researchers have proposed several typical recommendation algorithms based on social networks, such as social recommendation (SoRec), 17 recommendation with social trust ensemble (RSTE) 18 and a matrix factorization based model for recommendation in social rating networks (SocialMF) 19 and so forth.…”

Section: Recommendations Based On Social Networkmentioning

confidence: 99%

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

Wang

Wen

Zhou

et al. 2022

Concurrency and Computation

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With 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.

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A personalized recommender system for SaaS services

Afify

Moawad

Badr

et al. 2016

Concurrency and Computation

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Summary In this paper, we propose the Software‐as‐a‐Service (SaaS) Recommender (SaaSRec), a personalized reputation‐based QoS‐aware recommender system (RS) for SaaS services. SaaSRec semantically processes user requests in order to find business‐oriented matching services, which are then filtered to satisfy the user QoS requirements and service characteristics. Subsequently, hybrid filtering is utilized to validate the services set on the basis of services metadata, reputation and user interests. Finally, the recommended set of services is ranked using a unique combination of factors: Relevance to user profile, service reputations and service cost. Moreover, we propose a new method for calculating the service reputation from the objective time‐weighted user feedbacks. SaaSRec addresses many challenges faced by the generic RSs: User cold‐start problem, limited content analysis and low performance. In respect of service RSs, SaaSRec tackles the disregarding of relevant factors to services recommendation: Cloud service characteristics, user physical location, service reputation and user interests. Moreover, SaaSRec provides a hybrid justification for the recommended services to increase the user's acceptance. Experimental evaluation against a real‐world services dataset has been carried out, and the results show that the proposed recommendation approach surpasses other collaborative filtering‐based recommendation approaches in respect of both precision and recall. This performance improvement was verified using different matrix density levels and number of recommendations. Copyright © 2016 John Wiley & Sons, Ltd.

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Enhanced similarity measure for personalized cloud services recommendation

Cited by 12 publications

References 36 publications

A Service Recommendation Method Based on Requirements for the Cloud Environment

A Service Recommendation Method Based on Requirements for the Cloud Environment

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

A personalized recommender system for SaaS services

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