Towards a Comprehensive Web Service Recommendation Framework
Web services (WS) 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. The aim of this work is to advance the academic efforts in assisting end users and corporations to benefit from Web service technology by facilitating the recommendation and integration of Web services into composite services. In this thesis, we propose a recommendation framework that is capable of not only recommending an individual Web service but also a composite one when no service available to fulfill the user request. The framework is realized into two main parts. A recommendation model for individual WS is proposed where the QoS profile is considered as an implicit rating scheme. The model utilizes the Jaccard coefficient in several variants to create two Unipartite similarity-based graphs. By integrating them with the original user-service rating graph, a richer recommendation model is constructed. Using the Top-K Random Walk algorithm, a final set of recommendations is delivered to end user. The model proves its well-behaviour in terms of sparsity tolerance and recommendation accuracy. To minimize the complexity, a thresholding technique is proposed in which the Random Walk is better guided using a reduced subset of users based on their Jaccard similarities. Furthermore, the applicability of the model as a generic recommendation model is also examined using an ordinary rating domain. The second component is a service composition model in which AI-based planning using Agent technology is adopted to dynamically and flexibly construct composite service workflow. In this model, a distributed service dependency model based on AND/OR graph structure is decomposed and distributed among individual members of the Agent community. The agents are equipped with a well-defined internal reasoning mechanism based on agents' knowledge. Using a communication protocol, the agents actively collaborate to find a cost-effective executable workflow according to end user request. Finally, feasibility and effectiveness demonstration of all components of the proposed framework, using publicly available datasets, a recommendation library, and a multi-agent platform is verified.