International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 02 | Feb -2017
www.irjet.net
e-ISSN: 2395 -0056 p-ISSN: 2395-0072
A Privacy-Preserving QoS Prediction Framework for Web Service Recommendation Pallavi P. Gupta1, Sarika M. Chavan2 Dept of CSE (Deogiri Institute of Engineering and Management Studies) Aurangabad. pallavi.gupta26_07@yahoo.co.in 2 Dept of CSE (Deogiri Institute of Engineering and Management Studies) Aurangabad. sarikasolanke@dietms.org ---------------------------------------------------------------------***--------------------------------------------------------------------1
Abstract - Web service recommendation has become a hot
topic even in basic research in IT. The most popular technique is the collaborative filtering (CF) on the basis of a quality of service value. With the increasing presence and adoption of web services over the World Wide Web, the quality of service (QoS) is becoming more important to the description Nonfunctional characteristics of Web services. Several approaches for the selection of Web services and recommendation via collaborative filtering were studied; here we are going to investigate these techniques with the pros and cons of Techniques. Also based on these comments, we will propose a new technique for predicting the Web service selection based on known quality of service values and unknown we explain in our future work. Key Words: Web Service, Service Computing, Collaborative filtering, QoS values, Web service recommendation; QoS prediction; collaborative filtering; privacy preservation ‌
1.INTRODUCTION Web services are software components to support interoperable machine-to-machine interaction over a network. The increasing presence and acceptance of Web services on the World Wide Web demand effective recommendation and selection techniques that recommend the optimum web service users from a variety of available web services. With the number of Web services to increase Quality of Service (QoS) [1] is generally used to describe non-functional properties of Web services. Among the different QoS properties of Web services, some features are user independent and have identical values for different users (for example, price, popularity, availability, etc.). The values of the user independency of QoS properties are typically offered by service providers or third-party registers (for example, UDDI). On the other hand some QoS features users are dependent and have different values for different users (for example, response time, Invocation failure rate, etc.). Client-side Web service evaluation requires real web service calls and encounters the following drawbacks: 1) First, real Web service invocations impose costs for service users and consume the resources of the service provider. Some web service calls can also be charged. Š 2017, IRJET
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2) Secondly, it can exist on many Web service candidate analyzed and some appropriate web services in the evaluation list may not be detected and recorded by the service user. 3) Finally, most service users are not experts in web service evaluation and the common time-to-market constraints limiting an in-depth review of the target web service. However, without sufficient client-side evaluation, exact values of the user-specific QoS properties cannot be obtained. Optimal Web service selection and recommendation are so difficult to achieve.
2. RECOMMENDER SYSTEM User needs a special system which can understand their interests and suggest them the best usable services. In this case, Recommender systems can help users with the most suitable items to their interests, have been considered as one of the best solutions. Based on the functionality, the recommender systems can be classified as collaborative filtering, content based filtering, Hybrid models[2]. Recommender systems can help consumers and the most valuable items by calculating the similarities among other consumers with collaborative filtering algorithms.
2.1 Collaborative Filtering Methods
The process of identification of similar users, related Web services and recommend what similar users like is called collaborative filtering. The Web services for the user are based on the previous Web service history. A user can hardly recall all the services that the QoS (i.e. round-trip time RTT) represents, values of services that the user has not called are unknown. Therefore, and accurate Web service QoS forecast is very necessary for service user providers. Based on the predicted QoS values the desired service selection can be made. Collaborative Filtering[3] was initially proposed by Rich and has been widely used in service recommendation systems. In Web service recommendation, the primary question of the CF is to find a group of similar users, a group of similar services and user-service-matrix on the QoS value of services that is build by users. The user service matrix is actually very sparse in practice. Based on such a sparse matrix, the prediction accuracy of QoS values of services will decrease considerably. So we initially expected the QoS ISO 9001:2008 Certified Journal
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