Reputation measurement for online services based on CP-Nets learning and aggregation

The measurement of online service reputation based on ordinal preferences has been proposed to address the issue of unreliable reputation measurement results due to inconsistent user evaluation criteria. When users' complete ordinal preferences are unavailable, these methods ignore unknown preferences or use collaborative filtering to predict preferences without verifying the accuracy of preference prediction, leading to an untrustworthy service reputation. This study proposes an approach that models users' complete preferences using the conditional preference networks (CP-Nets) and then measures service reputation by aggregating CP-Nets. The approach designs an adaptive Tabu search algorithm to learn users' CP-Nets efficiently and aggregating all the CP-Nets using the ranked pairs method. The service reputation ranking is then deduced from the aggregated CP-Net. Experimental results on real datasets show that the proposed method is more efficient compared to existing methods, with more accurate preference prediction, and the reputation ranking is more consistent with user preferences.