Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization

Zheng, Zibin; Ma, Hao; Lyu, Michael R.; King, Irwin

doi:10.1109/tsc.2011.59

Cited by 399 publications

(230 citation statements)

References 41 publications

Supporting

Mentioning

227

Contrasting

Order By: Relevance

“…The only difference is that we predict response time by following formula (14) when . The formula (14) represents the traditional user-mean and item-mean methods. is the average response time value of the services invoked by the current active user u.…”

Section: The Response Time Prediction Algorithm For Current Activementioning

confidence: 99%

“…In this study, we use WS-DREAM dataset [14] to validate our prediction method. This dataset is composed of real historical QoS records of 100 distributed web service items invoked by 150 service users in different places.…”

Section: A the Experiments Designmentioning

confidence: 99%

“…The model-based method usually predefines a domain model for a given system, and then trains the model by existing training datasets for further QoS prediction. This kind of method can incorporate special domain knowledge and multiform artificial intelligence techniques to achieve higher prediction accuracy [13,14], but meanwhile has drawbacks of high complexity and limited universality.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Response Time Prediction of IoT Service Based on Time Similarity

Yang¹,

Zhang²

2017

Journal of Computing Science and Engineering

View full text Add to dashboard Cite

In the field of Internet of Things (IoT), smarter embedded devices offer functions via web services. The Quality-of-Service (QoS) prediction is a key measure that guarantees successful IoT service applications. In this study, a collaborative filtering method is presented for predicting response time of IoT service due to time-awareness characteristics of IoT. First, a calculation method of service response time similarity between different users is proposed. Then, to improve prediction accuracy, initial similarity values are adjusted and similar neighbors are selected by a similarity threshold. Finally, via a densified user-item matrix, service response time is predicted by collaborative filtering for current active users. The presented method is validated by experiments on a real web service QoS dataset. Experimental results indicate that better prediction accuracy can be achieved with the presented method.

show abstract

Section: The Response Time Prediction Algorithm For Current Activementioning

confidence: 99%

Section: A the Experiments Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Response Time Prediction of IoT Service Based on Time Similarity

Yang¹,

Zhang²

2017

Journal of Computing Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Zheng et al (2013), have proposed Collaborative Web service QoS prediction using neighborhood integrated matrix factorization [8]. The author applies the method of user collaboration for the Web service using QoS information sharing.…”

Section: Review Of Literaturementioning

confidence: 99%

Web Service Ranking using Rank Aggregation Method

Chavhan¹,

Badre²,

Kulkarni³

2017

IJARCSSE

View full text Add to dashboard Cite

Abstract-Web Service is the electronic processing of Internet communication system. The different applications communicate with each other to share data and services with the help of web services. The idea behind Web services is to make it as a practical tool to use the Internet as a visual tool. Web service fulfils both users functional and nonfunctional needs. Because of increase in the availability of web services, it is difficult to select appropriate candidates to serve the people in a huge number of web services. Hence, to choose and rank the web services we presented web service ranking approach using rank aggregation method by exploring the user behaviour. The invocation histories are used to infer the user behaviour. To rank the web services three aspects of web services Functional relevance, CF based scores, and the final QoS utility are considered. Moreover with the help of our system user gets recommendation according to his/her profile and current location.

show abstract

“…Chen et al [13] have propose a region-based hybrid collaborative filtering algorithm to predict the QoS values taking advantage of the great influence of user's location to the accuracy of prediction. Zibin' NIMF (neighborhood integrated matrix factorization) model takes advantage of the past Web service usage experience of service users to predict Web service QoS value for users [14]. Wei lo et al [15] have proposed an extended matrix factorization framework, this model is quite effective and scales to the large dataset.In [16],Chen et al have proposed an enhanced QoS prediction approach, which uses A-cosine equation for similarity calculation to remove the impact of different QoS scale and adds a data smoothing process to improve the prediction accuracy , to predict the missing QoS values.…”

Section: Related Workmentioning

confidence: 99%

Topic Model based Collaborative QoS Prediction

Wu¹,

Ji²,

Liangand³

et al. 2014

Appl. Math. Inf. Sci.

View full text Add to dashboard Cite

Abstract:With the increasing development and growth of Web services on the World Wide Web, the demand of appropriate Web service selection approaches are unprecedentedly strong, and Quality-of-Service (QoS) based service computing is becoming an important issue of service-oriented computing. In most of previous works, the QoS values of services to users are all conceived to be known, however, lots of them are unknown in practice application. Recently, lots of literatures aiming at predicting such missing QoS values are published, they all consider the unknown QoS values prediction as a fundamental step for the QoS-based service computing. Looking through existing works, we discover that the online cold-start scenario, in which some new coming Web services haven't been involved even once, hasn't been considered carefully. In this paper, we utilize a collaborative framework by integrating matrix factorization with probabilistic topic model to predict QoS values. Specifically, the basic idea of the proposed approach is collaborative filtering via matrix factorization, while the cold-start problem is handled by employing probabilistic topic model based on WSDL (Web Service Description Language) documents. The experiment are based on two real-world datasets (one contains 100 users and 150 Web services, and the other contains 339 users and 2344 Web services), and the results demonstrate the prediction accuracy of the proposed approach.

show abstract

Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization

Cited by 399 publications

References 41 publications

Response Time Prediction of IoT Service Based on Time Similarity

Response Time Prediction of IoT Service Based on Time Similarity

Web Service Ranking using Rank Aggregation Method

Topic Model based Collaborative QoS Prediction

Contact Info

Product

Resources

About