Personalized Location-Aware QoS Prediction for Web Services Using Probabilistic Matrix Factorization

Xu, Yueshen; Yin, Jianwei; Lo, Wei; Wu, Zhaohui

doi:10.1007/978-3-642-41230-1_20

Cited by 41 publications

(24 citation statements)

References 12 publications

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“…In this paper, we conduct experiments on both response time and throughput records. This dataset has been widely employed to evaluate the prediction accuracy by many researchers [18,24,30,31]. So the experimental results in this paper are convincing.…”

Section: Dataset and Evaluation Metricssupporting

confidence: 52%

“…The final prediction result is computed as the combination of the results that are achieved using the whole matrix and local matrices, respectively. Xu et al [24] extended the PMF (probabilistic matrix factorization) with geographical information. In their model, the similar neighbors were identified based on the geographical distance, and the latent feature vector of the target user was learned together with the feature vectors of similar neighbors.…”

Section: Related Workmentioning

confidence: 99%

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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: Dataset and Evaluation Metricssupporting

confidence: 52%

Section: Related Workmentioning

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 collaborative filtering (CF for short) method has been popularly used for QoS prediction [6][7][8][9]. There are two groups in the CF family, i.e., neighbor-based (focusing on identifying the similar relationship among users or services) and model-based (learning both the latent features of a user and a service and the relation between the latent features of users and services) [18][19][20][21][22][23][24][25][26][27].…”

Section: Related Workmentioning

confidence: 99%

“…By employing network information, they computed the network distances among users and further identified user neighborhoods. Xu et al [23] extended the PMF model (short for probabilistic matrix factorization) with geographical information [28]. Based on the geographical location of the target user, their method learned the user latent feature vector, investigating the impact of the features of similar neighbors.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Service Selection via Ensemble Learning in Mixed Mobile Network Environments

Yin

et al. 2017

Entropy

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Abstract:Mobile Service selection is an important but challenging problem in service and mobile computing. Quality of service (QoS) predication is a critical step in service selection in 5G network environments. The traditional methods, such as collaborative filtering (CF), suffer from a series of defects, such as failing to handle data sparsity. In mobile network environments, the abnormal QoS data are likely to result in inferior prediction accuracy. Unfortunately, these problems have not attracted enough attention, especially in a mixed mobile network environment with different network configurations, generations, or types. An ensemble learning method for predicting missing QoS in 5G network environments is proposed in this paper. There are two key principles: one is the newly proposed similarity computation method for identifying similar neighbors; the other is the extended ensemble learning model for discovering and filtering fake neighbors from the preliminary neighbors set. Moreover, three prediction models are also proposed, two individual models and one combination model. They are used for utilizing the user similar neighbors and servicing similar neighbors, respectively. Experimental results conducted in two real-world datasets show our approaches can produce superior prediction accuracy.

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“…A neighborhood based collaborative filtering approach is presented to predict unknown values for QoS-based service selection [10]. Most recently, some CF based service recommendation approaches employed the matrix factorization theory to improve the accuracy of QoS prediction [11,12]. A hybrid service recommendation approach is presented by combining collaborative filtering with content-based features of Web services [13].…”

Section: Related Workmentioning

confidence: 99%

An Approach to Detect Malicious Feedback Rating for Measuring Web Service Reputation

Kumar¹

2016

IJGDC

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Personalized Location-Aware QoS Prediction for Web Services Using Probabilistic Matrix Factorization

Cited by 41 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

Collaborative Service Selection via Ensemble Learning in Mixed Mobile Network Environments

An Approach to Detect Malicious Feedback Rating for Measuring Web Service Reputation

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