In recommender systems, bad recommendations can lead to a net utility loss for both users and content providers. The downside (individual loss) management is a crucial and important problem, but has long been ignored. We propose a method to identify bad recommendations by modeling the users' latent preferences that are yet to be captured using a residual model, which can be applied independently on top of existing recommendation algorithms. We include two components in the residual utility: benefit and cost, which can be learned simultaneously from users' observed interactions with the recommender system. We further classify user behavior into fine-grained categories, based on which an efficient optimization algorithm to estimate the benefit and cost using Bayesian partial order is proposed. By accurately calculating the utility users obtained from recommendations based on the benefit-cost analysis, we can infer the optimal threshold to determine the downside portion of the recommender system. We validate the proposed method by experimenting with real-world datasets and demonstrate that it can help to prevent bad recommendations from showing.