Abstract
Recent studies have shown that reputation escalation is emerging as a new service, by which dealers pay to receive good feedback and escalate their ratings in online shopping markets. With the dramatic increase in the number of ratings provided by consumers, scalability has arisen as a significant issue in the existing methods of reputation systems. In order to tackle such issue, we here propose a fast algorithm that calculates the reputation based on a random sample of the ratings. Since the randomly selected sample has a logarithmic size, it guarantees a feasible scalability for large-scale online review systems. In addition, the randomness nature of the algorithm makes it robust against unfair ratings. We analyze the effectiveness of the proposed algorithm through extensive empirical evaluation using real world and synthetically generated datasets. Our experimental results show that the proposed method provides a high accuracy while running much faster than the existing iterative filtering approach.
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