In online collaborative content generation systems, a group of contributors collaboratively generate artifacts. The main concern in these systems is the quality because of varying quality of human-generated contents. Several techniques and methods have been proposed for quality assessments in these systems. However, almost all of them are either based on prone to error techniques such as simple or weighted averaging, or they ignore the interrelation between the quality factors such as quality of artifacts and quality of contributors. In this paper, we present a novel iterative model for quality in collaborative content generation system. We then present an algorithm, based on our proposed quality model, that takes into account several factors such as popularity, community attention and relationships between artifacts and contributors, and computes meaningful accurate quality scores. We compare the performance of our model with a well-know selected work. The comparison results show the superiority of our model.

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