Popularity Based Web Service Search

Elfirdoussi, Selwa; Jarir, Zahi; Quafafou, Mohamed

doi:10.1109/icws.2012.87

Cited by 9 publications

(7 citation statements)

References 8 publications

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“…Another group of researchers were considering on the non-functional aspects such as web service selection and recommendation based on quality of service (QoS) [10], customer ratings [11] or service popularity [12]. However, the non-functional information is also always not available and these methods can only serve as a complement of service discovery.…”

Section: A Service Discoverymentioning

confidence: 99%

A Probabilistic Approach for Web Service Discovery

Zhang

Huai

et al. 2013

2013 IEEE International Conference on Services Computing

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Web service discovery is a vital problem in service computing with the increasing number of services. Existing service discovery approaches merely focus on WSDLbased keyword search, semantic matching based on domain knowledge or ontologies, or QoS-based recommendations. The keyword search omits the underlying correlations and semantic knowledge or QoS information is not always available. In this paper, we propose a probabilistic service discovery approach to help web service users to retrieve related services and to improve the search performance. Specifically, we apply a probabilistic model to characterize the latten topics between services and queries, and then propose a matching method based on the topic relevance. Experiments on services from a real service repository confirm the feasibility and efficiency of this proposed method.

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Section: A Service Discoverymentioning

confidence: 99%

A Probabilistic Approach for Web Service Discovery

Zhang

Huai

et al. 2013

2013 IEEE International Conference on Services Computing

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“…These approaches have a high degree of dependency on the service user's feedback ratings (Elfirdoussi et al, 2012;Bouguettaya, 2007, 2009a;Yuan et al, 2009;Trang et al, 2010;Sharma and Suman, 2013;Yao et al, 2013). Assessment of service reputation at QoS parameter level requires that each QoS parameter should be monitored and tracked dynamically because QoS may change over time.…”

Section: Introductionmentioning

confidence: 98%

“…Extensive research work has been performed on prediction of service reputation, such as collaborative filtering (CF), referral chain, contribution-based and previous QoS historical records Suman, 2014, 2013;Elfirdoussi et al, 2012;Li and Wang, 2011;Malik and Bouguettaya, 2009a;Wang et al, 2011b). Trust and reputation of a service from consumer's point of view can be the prediction about its future behaviour based on different consumer's rating over its transaction history.…”

Section: Introductionmentioning

confidence: 99%

A mixed opinion-based reputation prediction approach for reliable web service discovery

Rathore

Suman

2016

IJBIS

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Prediction of service reputation is an important research issue to include reliable services in service discovery and composition. A user of the service plays an important role in the prediction of service reputation. Therefore, feedback ratings provided by service users cannot be completely avoided. Most of the existing approaches fully trust on service users' feedback rating for reputation prediction, which often leads to bias towards positive or negative ratings. In this paper, a mixed opinion-based reputation prediction approach is proposed, which uses theoretical t-probability distribution and propositional logic to assess and predict the service reputation. The proposed approach combines users' feedback ratings and run time access rate to minimise the probable bias towards feedback ratings provided by users. Experimental result shows that proposed approach provides effective solution for prediction of service reputation, which can be helpful in performing reliable service discovery and composition.Reference to this paper should be made as follows: Rathore, M. and Suman, U. (2016) 'A mixed opinion-based reputation prediction approach for reliable web service discovery', Int.

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“…It stores important information into database related to discovered, invoked or published services which is helpful in finding frequency of used web services, their availability, usage history etc [3]. The trust among two or more users in the web of trust increases the quality of recommendation in two ways.…”

Section: Related Workmentioning

confidence: 99%