Colbar: A collaborative location-based regularization framework for QoS prediction

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

doi:10.1016/j.ins.2013.12.007

Cited by 95 publications

(44 citation statements)

References 26 publications

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“…Zhang et al [26] factorize user-service-time matrix of QoS using non-negative tensor factorization with time information. Yin et al [23] develop a location-based regularization framework for PMF prediction model. Lo et al [14] exploit PMF prediction model with a localtionbased pre-filtering stage on QoS matrix.…”

Section: Qos Prediction Based On Matrix Factorizationmentioning

confidence: 99%

Personalized QoS Prediction of Cloud Services via Learning Neighborhood-Based Model

et al. 2016

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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The explosion of cloud services on the Internet brings new challenges in service discovery and selection. Particularly, the demand for efficient quality-ofservice (QoS) evaluation is becoming urgently strong. To address this issue, this paper proposes neighborhoodbased approach for QoS prediction of cloud services by taking advantages of collaborative intelligence. Different from heuristic collaborative filtering and matrix factorization, we define a formal neighborhood-based prediction framework which allows an efficient global optimization scheme, and then exploit different baseline estimate component to improve predictive performance. To validate the proposed methods, a large-scale QoSspecific dataset which consists of invocation records from 339 service users on 5,825 web services on a worldscale distributed network is used. Experimental results demonstrate that the learned neighborhood-based models can overcome existing difficulties of heuristic collaborative filtering methods and achieve superior performance than state-of-the-art prediction methods.

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Section: Qos Prediction Based On Matrix Factorizationmentioning

confidence: 99%

Personalized QoS Prediction of Cloud Services via Learning Neighborhood-Based Model

et al. 2016

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…Lo et al (2012) build geographical regularization terms to make prediction. Moreover, Yin et al (2014) explain how to fuse different regularization parts into MF model. Even though some signals reveal geographical-based systems work in various situations, prediction accuracy still has plenty of room to grow in these frameworks, and how to incorporate geographical domain knowledge in systems is still an open question.…”

Section: Related Workmentioning

confidence: 99%

“…For a robust QoS prediction system, it is unacceptable to consume endless computation cost to generate high accuracy. In Yin et al (2014), the framework works less efficiently to gain more accuracy. To our best knowledge, limited works have been done to boost the solving process for QoS prediction.…”

Section: Related Workmentioning

confidence: 99%

Efficient web service QoS prediction using local neighborhood matrix factorization

Yin

Liu

et al. 2015

Engineering Applications of Artificial Intelligence

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“…On the one hand, people enjoy the convenience brought by the abundant information on the network; on the other hand, they are also suffering from the puzzle of information explosion. For example, if you are not good at filtering useless information when searching for the information you need from within a large amount of information, a considerable amount of time is spent in filtering information [4][5][6][7], which leads to a significant increase in retrieval time.…”

Section: Introductionmentioning

confidence: 99%

Design and analysis of a general vector space model for data classification in Internet of Things

Sang

Pang

Zha

et al. 2019

J Wireless Com Network

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The amount of information increases explosively in Internet of Things, because more and more data are sensed by large amount of sensors. The explosive growth of information makes it difficult to access information efficiently, so it is an effective method to decrease the amount of information to be transferred on network by text classification. This paper proposes a new text classification algorithm based on vector space model. This algorithm improves the feature selection and weighting methods by introducing synonym replacement to traditional text classification algorithms. The experimental results show that the proposed classification algorithm has considerably improved the precision and recall of classification.

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Colbar: A collaborative location-based regularization framework for QoS prediction

Cited by 95 publications

References 26 publications

Personalized QoS Prediction of Cloud Services via Learning Neighborhood-Based Model

Personalized QoS Prediction of Cloud Services via Learning Neighborhood-Based Model

Efficient web service QoS prediction using local neighborhood matrix factorization

Design and analysis of a general vector space model for data classification in Internet of Things

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