Statistical Learning of Domain-Specific Quality-of-Service Features from User Reviews

With the fast increase of online services of all kinds, users start to care more about the Quality of Service (QoS) that a service provider can offer besides the functionalities of the services. As a result, QoS-based service selection and recommendation have received significant attention since the mid-2000s. However, existing approaches primarily consider a small number of standard QoS parameters, most of which relate to the response time, fee, availability of services, and so on. As online services start to diversify significantly over different domains, these small set of QoS parameters will not be able to capture the different quality aspects that users truly care about over different domains. Most existing approaches for QoS data collection depend on the information from service providers, which are sensitive to the trustworthiness of the providers. Some service monitoring mechanisms collect QoS data through actual service invocations but may be affected by actual hardware/software configurations. In either case, domain-specific QoS data that capture what users truly care about have not been successfully collected or analyzed by existing works in service computing. To address this demanding issue, we develop a statistical learning approach to extract domain-specific QoS features from user-provided service reviews. In particular, we aim to classify user reviews based on their sentiment orientations into either a positive or negative category. Meanwhile, statistical feature selection is performed to identify statistically nontrivial terms from review text, which can serve as candidate QoS features. We also develop a topic models-based approach that automatically groups relevant terms and returns the term groups to users, where each term group corresponds to one high-level quality aspect of services. We have conducted extensive experiments on three real-world datasets to demonstrates the effectiveness of our approach.