Abstract
Online reputation has a strong impact on the success of a seller in an e-marketplace. Also, buyers use it to choose an appropriate seller among a set of alternatives. The standard practice of determining the reputation of a seller is the aggregation of the feedbacks or the ratings reported by its buyers. Such a model of reputation formulation is vulnerable to misleading and unfair feedbacks. A seller may collude with a set of buyers to report good feedbacks while the quality of its product is poor. Also the buyers can report unfair feedbacks being irrational, malicious or competitors. A robust reputation management mechanism is the one which can not be manipulated by these unfair feedbacks. The existing reputation management models are either reactive or proactive. The reactive solutions intend to identify the unfair feedbacks and the proactive solutions propose incentive to the buyers to encourage them to report fair feedbacks. In this paper, we propose an incentive system that encourages the buyers to report fair feedbacks. We associate a buyer’s reputation with a seller’s reputation if the buyer has expressed its feedback about the seller. If the reputation of the seller decreases then the reputation of all buyers who had endorsed it (provided positive feedbacks) also decreases and vice versa. This means a buyer risks its own reputation by providing the feedback about a seller. In this paper, we show that such a mechanism is incentive compatible, i.e., it encourages the buyers to provide fair feedbacks. Using analytical and experimental analysis, we show the correctness of this reputation management system.
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Notes
Our RMM uses specific user interface for buyers where the buyer have to explicitly choose between ignoring the task to submit feedback or chooses not to buy shares, i.e., expresses a negative feedback.
We do not consider random buyers in this data as it is not clear how to identify them for this data.
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Thakur, S. A reputation management mechanism that incorporates accountability in online ratings. Electron Commer Res 19, 23–57 (2019). https://doi.org/10.1007/s10660-017-9280-9
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DOI: https://doi.org/10.1007/s10660-017-9280-9