Robust Reputation-Based Ranking on Bipartite Rating Networks

With the growth of the Internet and E-commerce, bipartite rating networks are ubiquitous. In such bipartite rating networks, there exist two types of entities: the users and the objects, where users give ratings to objects. A fundamental problem in such networks is how to rank the objects by user’s ratings. Although it has been extensively studied in the past decade, the existing algorithms either cannot guarantee convergence, or are not robust to the spammers. In this paper, we propose six new reputation-based algorithms, where the users’ reputation is determined by the aggregated difference between the users’ ratings and the corresponding objects’ rankings. We prove that all of our algorithms converge into a unique fixed point. The time and space complexity of our algorithms are linear w.r.t. the size of the graph, thus they can be scalable to large datasets. Moreover, our algorithms are robust to the spamming users. We evaluate our algorithms using three real datasets. The experimental results confirm the effectiveness, efficiency, and robustness of our algorithms.