With the advancement of cloud computing and the proliferation of internet services, Quality of Service (QoS) has emerged as a critical metric for service caller, necessitating increasingly accurate predictive capabilities. While existing neural network methods have made significant progress, they have often overlooked the implicit complex interactions manifest in the distribution of QoS values. To overcome this deficiency, we introduce a Bipartite Graph-based Extension Modeling (BGEM) method with user-service interaction and a custom GCN module named Hidden Relationship Perception(HRP-GCN). In BGEM, we model the potential interactions between user-service and leverage the Jensen-Shannon divergence to quantify the similarity of QoS distribution patterns in users and services, resulting in the BGE-Graph. Subsequently, we employ a subgraph sampling algorithm to identify the optimal subgraph, and then fed it into HRP-GCN module for the prediction of unknown QoS values. Extensive experiments conducted on real-world datasets demonstrate that our proposed BGEM method outperforms current state-of-the-art approaches in QoS prediction, particularly under low-density conditions, where it still maintains superior performance.

With the rapid proliferation of Web services, accurately and efficiently predicting Quality of Service (QoS) has become a critical challenge in the field of service recommendation.However, existing deep learning approaches often focus on isolated feature types of either users or services and lack the capacity to comprehensively model the complex interactions between them.To address this limitation, this paper proposes a novel QoS prediction model named Multi-Feature-Aware Expert Modeling (MFAE).MFAE systematically extracts and processes five heterogeneous types of features associated with users, services, and their interactions: ID features, network topology features, geo-spatial features, similarity features, and 3-sigma-based outlier features.To effectively handle the heterogeneity among these feature types, the model constructs a network of expert groups, where each expert group consists of multiple Multi-Layer Perceptrons (MLPs) dedicated to deep representation learning for a specific feature category.The outputs from these expert groups are then fused through a weighted aggregation to generate the final QoS prediction.We evaluate MFAE on a real-world QoS dataset.Experimental results show that when the matrix density ranges from 2.5% to 10%, MFAE consistently achieves lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) compared to six baseline methods, demonstrating its effectiveness and robustness in sparse scenarios.

In the Internet of Things (IoT) environment, predicting the Quality of Service (QoS) is a challenging task due to the diversity of devices and service types. The dispersed nature of data collected from edge devices further complicates data sharing, thereby affecting the integrity and standardization of QoS evaluation. To address these issues, this paper proposes a QoS prediction model named HRMI, which efficiently integrates decentralized data and accommodates heterogeneous information to improve user experience. The HRMI framework comprises three main phases and one information-theoretic loss function: (1)Reputation Assessment, which leverages Utility Maximization theory and a Reputation Hybridization module to derive user and service reputations; (2)Feature Extraction, which employs deep learning techniques to construct user and service feature vectors from contextual attributes such as IDs, regions, and reputations; (3)Feature Interaction, which applies an attention mechanism to capture multi-scale feature dependencies and uncover deeper feature correlations; and (4)Mutual Information Loss, which exploits complementary information among feature vectors by maximizing the mutual information between feature representations and predicted values. Extensive experiments conducted on a widely used real-world dataset demonstrate that HRMI consistently outperforms state-of-the-art baseline methods.

With the surge of Web services, users are more concerned about Quality-of-Service (QoS) information when choosing Web services with similar functionalities. Today, effectively and accurately predicting QoS values is a tough challenge. Typically, traditional methods only use the QoS values provided by users to predict the missing QoS values, ignoring the arbitrariness of some users in providing observed QoS values and failing to consider the existence of anomalous QoS values with contingencies caused by some unstable Web services. Taking into account the above, this article proposes HyLoReF-us, a new framework for QoS prediction. HyLoReF-us uses the user reputation to measure the trustworthiness of users and the service reputation to measure the stability of web services. First, considering the utility generated by the invocation between users and Web services, HyLoReF-us employs a Logit model to calculate the user reputation and service reputation. Second, after combining the location information of users and services, as well as their reputations, HyLoReF-us obtains QoS predictions through an improved Matrix Factorization (MF) model. Finally, a series of experiments were conducted on the standard WS-DREAM dataset. Experimental results show that HyLoReF-us outperforms current state-of-the-art or baseline methods at Matrix Densities (MD) from 5% to 30%.

With the proliferation of Web services, users pay more attention to Quality of Service (QoS) information when choosing Web services with similar functionalities. Predicting QoS values effectively and accurately is a difficult challenge. In the real world, some users are strongly subjective in submitting QoS observations and some Web services suffer from instability caused by bugs. Therefore, this paper uses user reputation to measure the reliability of users and service reputation to measure the stability of Web services. We propose HyLoReF, a QoS prediction framework based on reputation and hybrid location information. HyLoReF uses Logit model to compute user reputation and service reputation, and combines them into Matrix Factorization (MF) model to improve the QoS prediction accuracy. Experimental results show that HyLoReF outperforms baseline methods and state-of-the-art models on the Web services standard dataset WS-DREAM [1] when the Matrix Density (MD) is in the interval of 5% to 30%.

As the homogenization of Web services becomes more and more common, the difficulty of service recommendation is gradually increasing. How to predict Quality of Service (QoS) more efficiently and accurately becomes an important challenge for service recommendation. Considering the excellent role of reputation and deep learning (DL) techniques in the field of QoS prediction, we propose a reputation and DL based QoS prediction network, RAHN, which contains the Reputation Calculation Module (RCM), the Latent Feature Extraction Module (LFEM), and the QoS Prediction Hourglass Network (QPHN). RCM obtains the user reputation and the service reputation by using a clustering algorithm and a Logit model. LFEM extracts latent features from known information to form an initial latent feature vector. QPHN aggregates latent feature vectors with different scales by using Attention Mechanism, and can be stacked multiple times to obtain the final latent feature vector for prediction. We evaluate RAHN on a real QoS dataset. The experimental results show that the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of RAHN are smaller than the six baseline methods.

Nowadays, there are many similar services available on the internet, making Quality of Service (QoS) a key concern for users.Since collecting QoS values for all services through user invocations is impractical, predicting QoS values is a more feasible approach.Matrix factorization is considered an effective prediction method.However, most existing matrix factorization algorithms focus on capturing global similarities between users and services, overlooking the local similarities between users and their similar neighbors, as well as the non-interactive effects between users and services.This paper proposes a matrix factorization approach based on user information entropy and region bias, which utilizes a similarity measurement method based on fuzzy information entropy to identify similar neighbors of users.Simultaneously, it integrates the region bias between each user and service linearly into matrix factorization to capture the noninteractive features between users and services.This method demonstrates improved predictive performance in more realistic and complex network environments.Additionally, numerous experiments are conducted on real-world QoS datasets.The experimental results show that the proposed method outperforms some of the state-of-the-art methods in the field at matrix densities ranging from 5% to 20%.

Facing a large number of candidate Web service with the same function, user wishes to get the most appropriate one.Quality-of-Service (QoS) which represents non-functional attributes of Web services, has become a major concern for choosing service.But it is time-consuming and resource-consuming to assess all the QoS values by invoking candidate services one by one.Thus, QoS prediction is considered an effective method to obtain QoS information.Although most of QoS prediction methods claim be able to capture the interaction between users and services, few of them take account non-interaction factors, especially the factors arising from the network environment.In this paper, the non-interaction factors from the network environment are referred as network bias, and a network biased matrix factorization (NBMF) method is proposed for QoS prediction.The method packages network bias into a linear regression model and puts the user-service interaction into a matrix factorization model, which is more sophisticated in adapting diversified circumstance, particularly in complex network environment.In addition, extensive experiments are conduct on real-world QoS dataset, and the result prove that the NBMF method achieves better performance than other state-ofthe-art methods.