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In this paper, we investigate how dynamic properties of reputation can influence the quality of users’ ranking. Reputation systems should be based on rules that can guarantee high level of trust and help identify unreliable units. To understand the effectiveness of dynamic properties in the evaluation of reputation, we propose our own model (DIB-RM) that utilizes three factors: forgetting, cumulative, and activity period. In order to evaluate the model, we use data from StackOverflow which also has its own reputation model. We estimate similarity of ratings between DIB-RM and the StackOverflow reputation model to test our hypothesis. We use two values to calculate our metrics: DIB-RM reputation and historical reputation. We found out that historical reputation gives better metric values. Our preliminary results are presented for different sets of values of the aforementioned factors in order to analyze how effectively the model can be used for modeling reputation systems.
Almaz Melnikov, JooYoung Lee, Victor Rivera, Manuel Mazzara, Luca Longo: Towards Dynamic Interaction-Based Reputation Models. IEEE 32nd International Conference on Advanced Information Networking and Applications, AINA 2018, pp.422-428 DOI: 10.1109/AINA.2018.00070