Online rating systems are widely accepted as a means for quality assessment on the web, and users increasingly rely on these systems when deciding to purchase an item online. This fact motivates people to manipulate rating systems by posting unfair rating scores for fame or profit. Therefore, both providing useful realistic rating scores as well as detecting unfair behaviours are of very high importance. Existing solutions are mostly majority based, also employing temporal analysis and clustering techniques. However, they are still vulnerable to unfair ratings. They also ignore distance between options, provenance of information and different dimensions of cast rating scores while computing aggregate rating scores and trustworthiness of raters. In this paper, we propose a robust iterative algorithm which leverages the information in the profile of raters, provenance of information and a prorating function for the distance between options to build more robust and informative rating scores for items as well as trustworthiness of raters. We have implemented and tested our rating method using both simulated data as well as three real world datasets. Our tests demonstrate that our model calculates realistic rating scores even in the presence of massive unfair ratings and outperforms well-known ranking algorithms.