  With increasing importance of e-commerce, many websites have emerged where
users can express their opinions about products, such as movies, books, songs,
etc. Such interactions can be modeled as bipartite graphs where the weight of
the directed edge from a user to a product denotes a rating that the user
imparts to the product. These graphs are used for recommendation systems and
discovering most reliable (trusted) products. For these applications, it is
important to capture the bias of a user when she is rating a product. Users
have inherent bias---many users always impart high ratings while many others
always rate poorly. It is necessary to know the bias of a reviewer while
reading the review of a product. It is equally important to compensate for this
bias while assigning a ranking for an object. In this paper, we propose an
algorithm to capture the bias of a user and then subdue it to compute the true
rating a product deserves. Experiments show the efficiency and effectiveness of
our system in capturing the bias of users and then computing the true ratings
of a product.
