Computing robust and accurate quality scores for users and items in online review systems is critical, since scores directly reflect the community-wide belief about their quality. A broad range of methods have been proposed to compute rating scores, including simple aggregation, weighted aggregation, and iterative techniques, where the latter provides relatively accurate results. However, there are still serious challenges to address, especially in terms of time complexity, accuracy, and robustness against manipulation. In this paper, we propose an adaptive quality assessment framework that computes dependable and accurate quality scores for users and items. The proposed method is a semi-iterative weighted aggregation technique in which, a novel approach is used to assign weights to received reviews. The weight depends on two parameters: similarity of reviews, and review prediction. In review prediction, we utilize a combination of online machine learning and collaborative filtering to predict the review expected from the user. The intuition behind using online learning is its ability to obtain lower time complexity in comparison with batch learning. We evaluate our proposed model using a real-word dataset, and compare it with two related approaches. Results show the superiority of our proposed approach, in terms of accuracy and robustness against manipulation.

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