Authenticity is the key for online review sites. Due to the significant development of review sites, the reviews are now highly important to users, producers and other stakeholders. Driven by interest, some imposters begin to post fake reviews to promote or discredit target products. The fake reviews not only mislead the users but also damage the service provider's credit. Current works mostly aim at classifying whether a specific review is fake or not, using context-based or user-based approaches. However, the aggregated rating of the product is viewer's most concern. Therefore, we propose a novel task to restore the truthful rating and further tackle it by statistical and deep learning techniques. We also assemble and publish a movie-review dataset for this task.

Online review sites are widely used for various domains including movies and restaurants. These sites now have strong influences towards users during purchasing processes. There exist plenty of research works for review sites on various aspects, including item recommendation, user behavior analysis, etc. However, due to the lack of complete and comprehensive dataset, there are still problems that remain to be solved. Therefore, in this paper we assemble and publish such dataset (CCMR) for the community. CCMR outruns existing datasets in terms of completeness, comprehensiveness and scale. Besides describing the dataset and its collecting methodology, we also propose several potential research topics that are made possible by having this dataset. Such topics include: (i) a statistical approach to reduce the impacts from fake reviews and (ii) analyzing and modeling the influences of public opinions towards users during rating actions. We further conduct preliminary analysis and experiments for both directions to show that they are promising.