Collaborative Based Filtering Approach for Web Service Recommendations using GEO-Locations

Service computing is one of Internet-based computing, whereas the shared configurable resources (e.g., infrastructure, platform, and software) are provided to computers and other devices are as services. Strongly promoted by the leading industrial companies like, Amazon, Google, Microsoft, IBM, etc, In recent years, service computing are quickly becoming popular. Applications are deployed in real time environment are typically large scale and complex. The rising popularity of service computing, it is how to build high-quality service applications it becomes an urgently required research problem. In Similar, the traditional component-based systems, cloud applications are typically involves multiple cloud components communicating with each other over application programming interfaces, through web services. On-functional performance of cloud services are usually described by the quality-of-service (QoS). QoS is an important research topic in cloud computing. When the creation optimal cloud service selection from a set of functionally corresponding services, QoS values are of cloud services provided the valuable information to assist decision making. The component-based systems, software components are invoked locally in tradition, while in cloud applications, the cloud services are invoked remotely by Internet connections. To evade the slow and expensive real-world service invocation QoS ranking prediction framework is used. This framework requires no extra invocations of cloud services when making QoS ranking prediction can implement novel collaborative filtering approach to recommend the web services with improved performance. DOI: 10.17762/ijritcc2321-8169.15033

QoS based Effective and Efficient Selection of Web Service and Retrieval of Search Information

Web services are integrated software components for the support of interoperable machine to machine interaction over a network. Web services have been widely employed for building service-oriented applications in both industry and academia in recent years. The number of publicly available Web services is steadily increasing on the Internet. However, this proliferation makes it hard for a user to select a proper Web service among a large amount of service candidates. An inappropriate service selection may cause many problems to the resulting applications. In this paper, a novel collaborative filtering-based Web service recommender system is proposed to help the users and select services with optimal QoS performance. Our recommender system employ an effective and efficient selection of web services and relevant retrieval of information and makes personalized service recommendation to users based on the clustering results. Compared with existing service recommendation methods, the proposed approach achieves considerable improvement on the recommendation accuracy and the QoS performance metrics adopted in this paper shows the better accuracy and relevant web services

An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks

QoS identification for untrustworthy Web services is critical in QoS management in the service computing since the performance of untrustworthy Web services may result in QoS downgrade. The key issue is to intelligently learn the characteristics of trustworthy Web services from different QoS levels, then to identify the untrustworthy ones according to the characteristics of QoS metrics. As one of the intelligent identification approaches, deep neural network has emerged as a powerful technique in recent years. In this paper, we propose a novel two-phase neural network model to identify the untrustworthy Web services. In the first phase, Web services are collected from the published QoS dataset. Then, we design a feedforward neural network model to build the classifier for Web services with different QoS levels. In the second phase, we employ a probabilistic neural network (PNN) model to identify the untrustworthy Web services from each classification. The experimental results show the proposed approach has 90.5% identification ratio far higher than other competing approaches.Comment: 8 pages, 5 figure

Web Service Recommender Systems: Methodologies, Merits and Demerits

Web services nowadays are considered a consolidated reality of the modern Web with remarkable, increasing influence on everyday computing tasks. Following Service-Oriented Architecture (SOA) paradigm, corporations are increasingly offering their services within and between organizations either on intranets or the cloud. Recommender Systems are the software agents guiding the web services to reach the end user. The aim of this paper is to present the survey of advancements in assisting end users and corporations to benefit from Web service technology by facilitating the recommendation and integration of Web services into composite services

Web service Recommendation by combining QOS and user comments

Due to well gaining experience of internet its user’s expectation from the search engines increases dramatically. Due to this search engines capability is not only limited to the providing desired URL’s to the users query. Moreover to this search engines are expected to provide information by analyzing in proper way like by doing surveys and recommendations. So many recommendation systems are existed which are working on some limited aspect of the parameters for recommending a web service. This paper represents a method of recommendation which considers users opinion and quality of the service parameter of the web service. The proposed idea captures the response time of the user transaction for a web service along with the users opinion comments about the web service. Then by combining both a new hybrid recommendation system is introduced which efficiently provides the recommendation that is more accurate and fine grained. This hybrid recommendation is powered with the Pearson correlation and strong NLP protocols to attain most accurate state. DOI: 10.17762/ijritcc2321-8169.15083

Decision support for personalized cloud service selection through multi-attribute trustworthiness evaluation

Facing a customer market with rising demands for cloud service dependability and security, trustworthiness evaluation techniques are becoming essential to cloud service selection. But these methods are out of the reach to most customers as they require considerable expertise. Additionally, since the cloud service evaluation is often a costly and time-consuming process, it is not practical to measure trustworthy attributes of all candidates for each customer. Many existing models cannot easily deal with cloud services which have very few historical records. In this paper, we propose a novel service selection approach in which the missing value prediction and the multi-attribute trustworthiness evaluation are commonly taken into account. By simply collecting limited historical records, the current approach is able to support the personalized trustworthy service selection. The experimental results also show that our approach performs much better than other competing ones with respect to the customer preference and expectation in trustworthiness assessment. © 2014 Ding et al