Collaborative Web Service QoS Prediction with Location-Based Regularization

Lo, Wei; Yin, Jianwei; Deng, Shuiguang; Liu, Ying; Wu, Zhaohui

doi:10.1109/icws.2012.49

Cited by 114 publications

(73 citation statements)

References 12 publications

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“…The goal of global filtering is to make the values of , , and close to each other. Some papers claim that the users with close geographical location have similar network environment and thus tend to experience similar QoS [17,29]. However, in mobile environment, the relation between the network configuration and location is more complex.…”

Section: Global Filtering With Similaritymentioning

confidence: 99%

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

Yin

et al. 2017

Mobile Information Systems

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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.

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Section: Global Filtering With Similaritymentioning

confidence: 99%

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

Yin

et al. 2017

Mobile Information Systems

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“…However, it is inefficient due to high economical expenses and computing time [4][5][6][7]. Hence, automatic mechanisms of Webservice selection become vital for industrial Web applications [3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Service selection should be taken according to various characteristics of Web-services [4][5][6][7][8][9][10][11]. Quality-of-service (QoS) is one of the most important characteristics of a Web-service [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

An Effective Scheme for QoS Estimation via Alternating Direction Method-Based Matrix Factorization

Luo

Zhou²,

Wang

et al. 2019

IEEE Trans. Serv. Comput.

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Abstract-Accurately estimating unknown quality-of-service (QoS) data based on historical records of Web-service invocations is vital for automatic service selection. This work presents an effective scheme for addressing this issue via alternating direction method-based matrix factorization. Its main idea consists of a) adopting the principle of the alternating direction method to decompose the task of building a matrix factorization-based QoS estimator into small subtasks, where each one trains a subset of desired parameters based on the latest status of the whole parameter set; b) building an ensemble of diversified single models with sophisticated diversifying and aggregating mechanism; and c) parallelizing the construction process of the ensemble to drastically reduce the time cost. Experimental results on two industrial QoS datasets demonstrate that with the proposed scheme, more accurate QoS estimates can be achieved than its peers with comparable computing time with the help of its easy parallel realization.

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“…Researchers have recently begun attending to the importance of providing new techniques for predicting QoS attributes for Web services [8], [9], [6], [10], [11]. Many QoS attributes of Web service have been considered and they provide different feasibility and usability [12], [3], [13].…”

Section: Related Workmentioning

confidence: 99%

“…In the area of selection and recommended systems, there are a number of systems using collaborative filtering for predicting unknown needed values [6], [9], [11], [15], [16], [17], [18], [19]. The collaborative filtering technique was coined by Goldberg et al in 1992 [20].…”

Section: Related Workmentioning

confidence: 99%

PLMwsp: Probabilistic Latent Model for Web Service QoS Prediction

Madi

Sheng

Yao

et al. 2016

2016 IEEE International Conference on Web Services (ICWS)

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Abstract-With the unprecedented and dramatic development of Web services in recent years, designing novel approaches for efficient Web service prediction has become of paramount importance. Quality of Service (QoS) plays a critical role in Web service recommendation. However determining QoS values of Web services is still a challenging task. For example, some QoS properties (e.g., response time, throughput) may hold different values for different users. In this paper, we describe how to develop a novel approach, PLMwsp, based on a probabilistic latent model, to predict effectively the QoS values of Web services. A Web service prediction has been developed, and experiments have been conducted to show the efficacy of our approach.

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Collaborative Web Service QoS Prediction with Location-Based Regularization

Cited by 114 publications

References 12 publications

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

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

An Effective Scheme for QoS Estimation via Alternating Direction Method-Based Matrix Factorization

PLMwsp: Probabilistic Latent Model for Web Service QoS Prediction

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