An approach towards debiasing user ratings
This addresses bias in e-commerce recommendation systems, but appears incremental as it builds on existing bipartite graph models.
The paper tackled the problem of user bias in rating systems by proposing an algorithm to capture and compensate for individual biases to compute true product ratings, demonstrating efficiency and effectiveness in experiments.
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.