Personalized QoS-Aware Web Service Recommendation and Visualization

Chen, Xi; Zheng, Zibin; Li, Xudong; Huang, Zicheng; Sun, Hailong

doi:10.1109/tsc.2011.35

Cited by 171 publications

(117 citation statements)

References 31 publications

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“…A. CF Methods: The memory based mostly and model based ways square measure two different sort of CF techniques that square measure universally utilized in recommendation systems. Well known memory based mostly ways include user {based|based mostly|primarily based mostly} approaches [7] and item based approaches [8]. Memory based cooperative Filtering techniques have been recently adopted to provide QoS-aware recommendations [9,10].…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, there are service shoppers WHO antecedently have experience in victimization net services and so will facilitate in selecting services with adequate quality. This principle is extensively used by (collaborative) recommendation and reputation systems [6] [7]. Often net services are adapt not on public use but aim at enabling simple info exchange between a set of partner organizations.…”

Section: Introductionmentioning

confidence: 99%

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QoS Based Approach for Web Service Recommendation

Chame¹,

Deshpande²

2016

IJECS

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Web services are important integrated software components for the support of interoperable machine to machine communication over internet. Web services have been widely used for building services oriented applications in industry in recent years. On internet publically available web services are widely increased in recent years. For the web services users it is very hard to select a proper web services among a large amount of services available on internet. Improper selection of web services may cause many problems (e.g., illsuited performance) to the resulting applications. In this paper we proposed Collaborative Filtering (CF), we propose an innovative CF algorithm for Q-o-S-based web service recommendation. we provide a personalized map for browsing the recommendation results. The map explicitly shows the QoS relationships of the recommended web services as well as the basic structure of the QoS space by using map metaphor such as dots, areas, and spatial arrangement.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

QoS Based Approach for Web Service Recommendation

Chame¹,

Deshpande²

2016

IJECS

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show abstract

“…Based on the predicted QoS values of Web services, personalized QoS-aware Web service recommendations can be produced to help users select the optimal service among the functionally equivalent ones [3].…”

Section: IIImentioning

confidence: 99%

Web Service Recommendation Using QOS Parameters and Users Location

Patil¹,

pawar²,

More³

et al. 2015

International Journal of Advanced Research in Computer and Comm

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Abstract:A Web service is a method of communication between two electronic devices over a network. Web services have been widely employed for building service-oriented applications. Recommendation techniques are very important in the fields of E-commerce and other Web-based services. One of the main difficulties is dynamically providing highquality recommendation on sparse data. In this paper, a novel collaborative filtering-based Web service recommendation algorithm is proposed, in which information contained in both ratings and profile contents are utilized by exploring latent relations between ratings, a set of dynamic features are designed to describe user preferences in multiple phases, and finally a recommendation is made by adaptively weighting the features.

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“…Cosine-based approach (COS) [14,15] and Pearson Correlation Coefficient (PCC) [12,13] are two of the most popular memory-based approaches [16] to calculate the similarity between items. A number of works that employ COS-CF (Cosine based Collaborative-filtering) or PCC-CF (Pearson Correlation Coefficient based Collaborative filtering) for QoS based service recommendation and selection have been proposed recently [11] [17][18][19]. However, the performance that using PCC and COS to measure similarity leaves much to be desired and the prediction accuracy of these works cannot satisfy the requirement of practical application.…”

Section: Motivationmentioning

confidence: 99%

“…Moreover, the experiments of these existing works are not convincing enough. The existing service recommendation [17,19] approaches are short of sufficient-scale and systematic evaluation to verify their recommendation results. Some of them employ item dataset (such as MovieLens [20]) instead of real service dataset to evaluate their approaches.…”

Section: Motivationmentioning

confidence: 99%

A Feedback-corrected Collaborative Filtering for Personalized Real-world Service Recommendation

Zhao

Zhang

Cheng

et al. 2014

INT J COMPUT COMMUN

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The emergence of Internet of Things (IoT) integrates the cyberspace with the physical space. With the rapid development of IoT, large amounts of IoT services are provided by various IoT middleware solutions. So, discovery and selecting the adequate services becomes a time-consuming and challenging task. This paper proposes a novel similarity-measurement for computing the similarity between services and introduces a new personalized recommendation approach for real-world service based on collaborative filtering. In order to evaluate the performance of proposed recommendation approach, large-scale of experiments are conducted, which involves the QoS-records of 339 users and 5825 real web-services. The experiments results indicate that the proposed approach outperforms other compared approaches in terms of accuracy and stability.

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Personalized QoS-Aware Web Service Recommendation and Visualization

Cited by 171 publications

References 31 publications

QoS Based Approach for Web Service Recommendation

QoS Based Approach for Web Service Recommendation

Web Service Recommendation Using QOS Parameters and Users Location

A Feedback-corrected Collaborative Filtering for Personalized Real-world Service Recommendation

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