Collaborative QoS Prediction Research Articles

Privacy-preserving shared collaborative web services QoS prediction

Collaborative Web services QoS prediction (CQoSP) has been proved to be an effective tool to predict unknown QoS values of services. Recently a number of efforts have been made in this area, focusing on improving the accuracy of prediction. In this paper, we consider a novel kind of CQoSP, shared CQoSP, where multiple parties share their data with each other in order to provide more accurate prediction than a single party could do. To encourage data sharing, we propose a privacy-preserving framework which enables shared collaborative QoS prediction without leaking the private information of the involved party. Our framework is based on differential privacy, a rigorous and provable privacy model. We conduct extensive experiments on a real Web services QoS dataset. Experimental results show the proposed framework increases prediction accuracy while ensuring the privacy of data owners.

Differential private collaborative Web services QoS prediction

Collaborative Web services QoS prediction has proved to be an important tool to estimate accurately personalized QoS experienced by individual users, which is beneficial for a variety of operations in the service ecosystem, such as service selection, composition and recommendation. While a number of achievements have been attained on the study of improving the accuracy of collaborative QoS prediction, little work has been done for protecting user privacy in this process. In this paper, we propose a privacy-preserving collaborative QoS prediction framework which can protect the private data of users while retaining the ability of generating accurate QoS prediction. We introduce differential privacy, a rigorous and provable privacy model, into the process of collaborative QoS prediction. We first present DPS, a method that disguises a user’s observed QoS values by applying differential privacy to the user’s QoS data directly. We show how to integrate DPS with two representative collaborative QoS prediction approaches. To improve the utility of the disguised QoS data, we present DPA, another QoS disguising method which first aggregates a user’s QoS data before adding noise to achieve differential privacy. We evaluate the proposed methods by conducting extensive experiments on a real world Web services QoS dataset. Experimental results show our approach is feasible in practice.

Collaborative QoS prediction with context-sensitive matrix factorization

How to obtain personalized quality of cloud/IoT services and assist users selecting the appropriate service has become a hot issue with the explosion of services on the Internet. Collaborative QoS prediction is proposed to address this issue by borrowing ideas from recommender systems. However, there is still a challenging problem as how to incorporate contextual factors into existing algorithms to realize context-aware QoS prediction as contextual factors play a crucial role in QoS assessment. In this paper, we propose a general context-sensitive matrix-factorization approach (CSMF) to make collaborative QoS prediction. By considering the complexity of service invocations, CSMF models the interactions of users-to-services and environment-to-environment simultaneously, and make full use of implicit and explicit contextual factors in the QoS data. Experimental results show that CSMF significantly outperforms the-state-of-art methods in metric of prediction accuracy. Particularly, when the QoS data is very sparse, CSMF is more effective and robust.

Collaborative QoS Prediction for Mobile Service with Data Filtering and SlopeOne Model

The mobile service is a widely used carrier for mobile applications. With the increase of the number of mobile services, for service recommendation and selection, the nonfunctional properties (also known as quality of service, QoS) become increasingly important. However, in many cases, the number of mobile services invoked by a user is quite limited, which leads to the large number of missing QoS values. In recent years, many prediction algorithms, such as algorithms extended from collaborative filtering (CF), are proposed to predict QoS values. However, the ideas of most existing algorithms are borrowed from the recommender system community, not specific for mobile service. In this paper, we first propose a data filtering-extended SlopeOne model (filtering-based CF), which is based on the characteristics of a mobile service and considers the relation with location. Also, using the data filtering technique in FB-CF and matrix factorization (MF), this paper proposes another model FB-MF (filtering-based MF). We also build an ensemble model, which combines the prediction results of FB-CF model and FB-MF model. We conduct sufficient experiments, and the experimental results demonstrate that our models outperform all compared methods and achieve good results in high data sparsity scenario.

Deviation-based neighborhood model for context-aware QoS prediction of cloud and IoT services

How to obtain personalized quality of cloud/IoT services and assist users selecting appropriate service has grown up to be a hot issue with the explosion of services on the Internet. Collaborative QoS prediction is recently proposed addressing this issue by borrowing ideas from recommender systems. Going down this principle, we propose novel deviation-based neighborhood models for QoS prediction by taking advantages of crowd intelligence. Different from existing works, our models are under a two-tier formal framework which allows an efficient global optimization of the model parameters. The first component gives a baseline estimate for QoS prediction using deviations of the services and the users. The second component is founded on the principle of neighborhood-based collaborative filtering and contributes fine-grained adjustments of the predictions. Also, contextual information is used in the neighborhood component to strengthen the predicting ability of the proposed models. Experimental results, on a large-scale QoS-specific dataset, demonstrate that deviation-based neighborhood models can overcome existing difficulties of heuristic collaborative filtering methods and achieve superior performance than the state-of-the-art prediction methods. Also, the proposed models can naturally exploit location information to ensure more accurate prediction results.

Exploiting Web service geographical neighborhood for collaborative QoS prediction

Since Web services with equivalent functionalities but different quality are becoming increasingly available on the Internet, predicting the unknown QoS value of a Web service to an active user who has not accessed the service previously is often required for Web service recommendation and composition. Existing collaborative filtering methods suffer from the unavoidable sparsity and cold-start problems and underestimate the role of geographical information that inherently exists in user–service rating oriented model. The principal motivation for using geographical information in Web service QoS prediction stems from the observation that the ratings Web services perform are influenced significantly by their geographical neighborhood, a fact that is verified by our empirical data analysis on the real-world QoS dataset WSDream. Hence, it will be of interest to incorporate this implicit source of information in QoS prediction. In this paper, carefully selected geographical neighbors, clustered using a bottom-up hierarchical neighborhood clustering method, are smoothly integrated into a matrix factorization model, thereby building a more accurate prediction model. Further accuracy improvements are achieved by considering the biases of users and Web services. In experiments using the WSDream QoS dataset, our proposed method outperforms the other competitive methods with respect to accuracy and alleviates the sparsity and cold-start issues.