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Proceedings ArticleDOI

A Novel Approach for Web Service Recommendation

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TLDR
This paper proposed a novel approach for web service recommendation system to work through the problem of overflow of web services over internet and found it to be need of the hour.
Abstract
Businesses are increasingly using web services to expose and exchange data over the web. Last few years have viewed tremendous growth in web service as a technology for inter application logic sharing and data retrieval. As web services have been able to provide simple solutions of very generic business problems it has got much higher acceptance and presence over the internet. Today internet is flooded with web services which give rise to problem of selection of web service that will best fit the business use case. To work through the problem of overflow of web services over internet a very efficient web service selection mechanism is need of the hour. In this paper we proposed a novel approach for web service recommendation system.

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Citations
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Journal ArticleDOI

Deep knowledge-aware framework for web service recommendation

TL;DR: A deep knowledge-aware approach is proposed which introduces knowledge graph and knowledge representation into web service recommendation for the first time and can achieve better recommendation performance than other state-of-the-art methods.
References
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Journal ArticleDOI

A survey of collaborative filtering techniques

TL;DR: From basic techniques to the state-of-the-art, this paper attempts to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.
Journal ArticleDOI

Discrete Bayesian Network Classifiers: A Survey

TL;DR: This article surveys the whole set of discrete Bayesian network classifiers devised to date, organized in increasing order of structure complexity: naive Bayes, selective naive Baye, seminaive Bayer, one-dependence Bayesian classifiers, k-dependency Bayesianclassifiers, Bayes network-augmented naiveBayes, Markov blanket-based Bayesian Classifier, unrestricted BayesianClassifiers, and Bayesian multinets.
Journal ArticleDOI

Web Service Recommendation via Exploiting Location and QoS Information

TL;DR: This paper proposes a novel collaborative filtering-based Web service recommender system to help users select services with optimal Quality-of-Service (QoS) performance, and achieves considerable improvement on the recommendation accuracy.
Journal ArticleDOI

Unified Collaborative and Content-Based Web Service Recommendation

TL;DR: This paper proposes a novel approach that unifies collaborative filtering and content-based recommendation of web services using a probabilistic generative model, which outperforms the state-of-the-art methods on recommendation performance.
Journal ArticleDOI

Location-Aware and Personalized Collaborative Filtering for Web Service Recommendation

TL;DR: The experimental results indicate that the proposed location-aware personalized CF method improves the QoS prediction accuracy and computational efficiency significantly, compared to previous CF-based methods.