Recommending Web Service Based on User Relationships and Preferences

Min, Gong; Xu, Zhaogui; Xu, Lei; Li, Yanhui; Chen, Lin

doi:10.1109/icws.2013.58

Cited by 19 publications

(5 citation statements)

References 15 publications

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“…In this authors incorporate user's probable QoS recommendations and also various quality features of user's interest in Web services depending on exploring the user's history of the Web Service. Gong et al (2013) give service recommendation an approach is recognized comparatively URPC-Rec (User Relationships Preferences Clustering also Recommendation). In this algorithm, requested services depending on their history behaviors used by clusters and after that specification is given.…”

Section: Related Workmentioning

confidence: 99%

Novel Clustering-Based Web Service Recommendation Framework

Pandharbale

Mohanty²,

Jagadev

2021

International Journal of System Dynamics Applications

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Normally web services are classified originate in on the quality of service, wherever the term quality is not absolute and it is a relative term. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, availability, etc. However, the limitation of these methods is that they are producing similar web services in recommendation lists some times. To address this research problem, the novel improved the Clustering-based web service recommendation method is proposed in this project. This approach is mainly dealing to produce diversity in the results of web service recommendation. In this method, functional interest, QoS preference, and diversity features are combined to produce the unique recommendation list of web services to end-users. To produce the unique recommendation results, we proposed a vary web service classify order that is clustering-based on web services' functional relevance such as non-useful pertinence, recorded client intrigue importance, potential client intrigue significance, etc.

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Section: Related Workmentioning

confidence: 99%

Novel Clustering-Based Web Service Recommendation Framework

Pandharbale

Mohanty²,

Jagadev

2021

International Journal of System Dynamics Applications

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show abstract

“…Individual service recommendation is a hot research spot nowadays. Gong et al [196] presented a service recommendation algorithm URPC‐Rec, which first clusters users based on their history behaviours, and then makes personalised recommendations for users considering both the clustering results, user basic information and relationships.…”

Section: Nature‐inspired Computing (Nic) Approaches For Web Service Cmentioning

confidence: 99%

Advances on QoS‐aware web service selection and composition with nature‐inspired computing

Zhao

Zuo

2019

CAAI Transactions on Intelligence Technology

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“…The main goal of service recommendation approach is to help the users discover the services that meet their needs. CF is one of the most popular approaches to build service recommender system [9,10,43]. However, with the advent of social networks, the SR approach can potentially solve some challenging problems of CF approach, such as the data sparsity and the cold-start problems [14].…”

Section: Service Recommendation Approachesmentioning

confidence: 99%