Personalized Cloud Service Review Ranking Approach Based on Probabilistic Ontology

Online cloud service reviews have recently gained an increasing attention since they can have a significant impact on cloud user’ purchasing decision. A large number of cloud users consult these reviews before choosing cloud services. Therefore, identifying the most-helpful reviews is an important task for online retailers. The helpfulness of product/service reviews has been widely investigated in the marketing domain. However, these works do not pay attention to the following significant points: (1) the heterogeneity problem when extracting information from different Social Media Platforms (SMP), (2) the uncertainty judgment of review helpfulness and (3) the personalizing of review ranking by considering the context of the review. To tackle these three points we propose a new approach that relies on probabilistic ontology, called Context-aware Review Helpfulness Probabilistic Ontology (C-RHPO), to cope with the heterogeneity and uncertainty issues. In addition, the approach uses a personalized online review ranking method based on the end-user context. The herein reported experimental results proved the effectiveness and the performance of the approach.