AN EFFICIENT SIMILARITY BASED MODEL FOR WEB SERVICE RECOMMENDATION
With the emergence of Cloud and Mobile Computing paradigms which adopt web services (WS) as a means of management, companies worldwide are actively deploying web services within their business environments. Consequently, designing effective Web service recommendation mechanisms is receiving more research attention. Traditional Neighborhood based Collaborative Filtering (CF) models fail to capture the actual relationships between users and services due to data sparsity. In contrast, Random Walk (RW) algorithm, which is categorized as a sparsitytolerant recommendation approach, suffers from poor recommendation accuracy. In this paper, we first propose an Integrated Model QoS based Graph (IMQG) in which users and services represent nodes while weighted Quality of Service (QoS) magnitudes and User/Service similarity measurements serve as edges. Variants of Jaccard coefficient are used to separately compute these similarities. Then, Top k Random Walk algorithm is applied to generate a final recommendation list. Further, we reduce the model by selecting only a subset of users to better guide RW algorithm. Comprehensive experiments are conducted on a real world dataset where analysis of results shows high improvement in recommendation accuracy with more tolerance to data sparsity. Utilizing the improved model, a considerable reduction in computation time is achieved while maintaining the recommendation accuracy.