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.

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%.