Bayesian Network Reasoning

In the domain of service-oriented computing, it is crucial to suggest not only individual atomic services but also a suite of interrelated services that align with users’ Quality of Service (QoS) expectations. To optimize the organization and recommendation of web services, our approach employs a three-tiered Bayesian network structure learning process. This process is designed to formulate a directed acyclic graph (network) and then engages in parameter learning to deduce the conditional probabilities across all network nodes. Our utilization of the Bayesian network, known for its adept handling of service correlations, represents a more appropriate choice for recommending interconnected services when compared to alternative methods. We conducted thorough experiments to validate our proposed method, demonstrating its capabilities and benefits.