Personalized Quality Of Service Prediction Research Articles

BGCL: Bi-subgraph network based on graph contrastive learning for cold-start QoS prediction

With the advent of Web service technologies, the number of services published on the cloud is increasing rapidly. The quality of service (QoS) becomes a crucial criterion for selecting services from a massive pool of candidates. Collaborative filtering (CF) has become a major way for personalized QoS prediction by leveraging historical interactions between users and services. Due to the increasing number of users and services, CF-based QoS prediction often suffers from data sparsity and cold-start difficulties. Inspired by the advantages of graph contrastive learning in cold-start predictions, we propose BGCL, a bi-subgraph network based on graph contrastive learning to solve the above problems. Firstly, we generate different perspectives of user-neighborhood and service-neighborhood sub-graphs based on sparse user–service bipartite graphs. Next, our model learns user and service embeddings using the graph contrastive learning and graph attention aggregation mechanisms on the generated sub-graphs. Finally, user and service embeddings are fed into a multi-layer perception to predict QoS values. Experimental results show that our model outperforms several existing models in terms of prediction accuracy.

A Personalized QoS Prediction Method for Web Services via Blockchain-Based Matrix Factorization.

Personalized quality of service (QoS) prediction plays an important role in helping users build high-quality service-oriented systems. To obtain accurate prediction results, many approaches have been investigated in recent years. However, these approaches do not fully address untrustworthy QoS values submitted by unreliable users, leading to inaccurate predictions. To address this issue, inspired by blockchain with distributed ledger technology, distributed consensus mechanisms, encryption algorithms, etc., we propose a personalized QoS prediction method for web services that we call blockchain-based matrix factorization (BMF). We develop a user verification approach based on homomorphic hash, and use the Byzantine agreement to remove unreliable users. Then, matrix factorization is employed to improve the accuracy of predictions and we evaluate the proposed BMF on a real-world web services dataset. Experimental results show that the proposed method significantly outperforms existing approaches, making it much more effective than traditional techniques.

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.

Personalized QoS Prediction for Service Recommendation With a Service-Oriented Tensor Model

Quality of Service (QoS) value is usually unknown in service recommendation practice. There are some matrix factorization approaches for predicting the unknown value with a user-service model, which uses a single collaboration with the user’s neighbor when looking for different services. However, the QoS value is highly related to the service provider and participants. The services are considered in various collaboration based on different users. By considering the context of services, this paper proposes a QoS prediction model using tensor decomposition based on service collaboration called Service-oriented Tensor (SOT). The prediction approach analyzes service collaboration from other similar services and relevant users by using a three-order tensor. Compared with the traditional model, the experiment results show that the proposed model achieves better prediction accuracy.

A novel QoS-aware prediction approach for dynamic web services.

Web service has become irreplaceable for service-oriented application in both academia and industry in recent years. Quality of Service (QoS) is used to describe the nonfunctional characteristics of Web service. Identifying Web service QoS is crucial for service-oriented application designers because service users may obtain very different QoS performance of the same service in the client-side due to dynamic changes of Internet environment as well as user context. However, evaluating QoS performance of a large scale of Web services requires considerable time and resources in real-world. Existing methods can make a personalized prediction for average QoS values by employing historical data but fail to take into consideration the fluctuation feature of Web service QoS values. To address this issue, this paper proposes a novel method for personalized QoS prediction of dynamic Web Services. First, a novel approach is used to extract feature points of QoS sequences and dynamic time warping distance is used to compute the similarity instead of Euclidean distance. By finding the most similar QoS sequences of the target QoS sequence, the missing QoS values can be predicted without extra Web services invoking. To validate our method, we conduct a large number of experiments based on real-world Web service QoS data set. The experimental studies show that our method has higher accuracy rate compared with the existing methods.

A Personalized QoS Prediction Approach for CPS Service Recommendation Based on Reputation and Location-Aware Collaborative Filtering.

With the rapid development of cyber-physical systems (CPS), building cyber-physical systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedure of building Cyber-physical systems, it has been found that a large number of functionally equivalent services exist, so it becomes an urgent task to recommend suitable services from the large number of services available in CPS. However, since it is time-consuming, and even impractical, for a single user to invoke all of the services in CPS to experience their QoS, a robust QoS prediction method is needed to predict unknown QoS values. A commonly used method in QoS prediction is collaborative filtering, however, it is hard to deal with the data sparsity and cold start problem, and meanwhile most of the existing methods ignore the data credibility issue. Thence, in order to solve both of these challenging problems, in this paper, we design a framework of QoS prediction for CPS services, and propose a personalized QoS prediction approach based on reputation and location-aware collaborative filtering. Our approach first calculates the reputation of users by using the Dirichlet probability distribution, so as to identify untrusted users and process their unreliable data, and then it digs out the geographic neighborhood in three levels to improve the similarity calculation of users and services. Finally, the data from geographical neighbors of users and services are fused to predict the unknown QoS values. The experiments using real datasets show that our proposed approach outperforms other existing methods in terms of accuracy, efficiency, and robustness.

TAP: A personalized trust-aware QoS prediction approach for web service recommendation

With the rapid development of service-oriented computing, cloud computing and big data, a large number of functionally equivalent web services are available on the Internet. Quality of Service (QoS) becomes a differentiating point of services to attract customers. Since the QoS of services varies widely among users due to the unpredicted network, physical location and other objective factors, many Collaborative Filtering based approaches are recently proposed to predict the unknown QoS by employing the historical user-contributed QoS data. However, most existing approaches ignore the data credibility problem and are thus vulnerable to the unreliable QoS data contributed by dishonest users. To address this problem, we propose a trust-aware approach TAP for reliable personalized QoS prediction. Firstly, we cluster the users and calculate the reputation of users based on the clustering information by a beta reputation system. Secondly, a set of trustworthy similar users is identified according to the calculated user reputation and similarity. Finally, we identify a set of similar services by clustering the services and make prediction for active users by combining the QoS data of the trustworthy similar users and similar services. Comprehensive real-world experiments are conducted to demonstrate the effectiveness and robustness of our approach compared with other state-of-the-art approaches.

Efficient web service QoS prediction using local neighborhood matrix factorization

In the era of Big Data, companies worldwide are actively deploying web services in both intranet and internet environments. Quality-of-Service (QoS), the fundamental aspect of web service has thus attracted numerous attention in industry and academia. The study on sufficient QoS data keeps advancing the state in Service-Oriented Computing (SOC) area. To collect a large amount of resource in practice, QoS prediction applications are designed and built. Nevertheless, how to generate accurate results in high productivity is still a main challenge to existing frameworks. In this paper, we propose LoNMF, a Local Neighborhood Matrix Factorization application that incorporates domain knowledge in modern Artificial Intelligence (AI) technique to tackle this challenge. LoNMF first proposes a two-level selection mechanism that can identify a set of highly relevant local neighbors for target user. And then, it integrates the geographical information to build up an extended Matrix Factorization (MF) approach for personalized QoS prediction. Finally, it iteratively generates results by utilizing hints from previous round computations, a gradient boosting strategy that directly accelerates solving process. Experimental evidence on large-scale real-world QoS data shows that LoNMF is scalable, and consistently outperforming other state-of-the-art applications in prediction accuracy and efficiency.

Colbar: A collaborative location-based regularization framework for QoS prediction

Quality-of-Service (QoS) is a fundamental element in Service-Oriented Computing (SOC) domain. At the ongoing age of Web 2.0, predicting the missing QoS values becomes more and more important since it is an indispensable preprocess of numerous service-oriented applications. Previous research works on this task underestimate the importance of users’ geographical information, which we argue would contribute to improving prediction accuracy in Web services invocation process. In this paper, we propose a novel collaborative location-based regularization framework (Colbar) to address the problem of personalized QoS prediction. We first leverage the personal geographical and QoS information to identify robust neighborhoods. And then, we collect the wisdom of crowds to construct two location-based regularization terms, which are integrated to build up an unified Matrix Factorization framework. Finally we make intermediate fusions to generate better prediction results. The experimental analysis on a large-scale real-world QoS dataset shows that the prediction accuracy of Colbar outperforms other state-of-the-art approaches in various criteria.