Controversial issues often split the population into groups with opposing views. When such issues emerge on social media, we often observe the creation of "echo chambers," i.e., situations where like-minded people reinforce each other's opinion, but do not get exposed to the views of the opposing side. In this paper we study algorithmic techniques for bridging these chambers, and thus reduce controversy. Specifically, we represent discussions as graphs, and cast our objective as an edge-recommendation problem. The goal of the recommendation is to reduce the controversy score of the graph, measured by a recently-developed metric based on random walks. At the same time, we take into account the acceptance probability of the recommended edges, which represent the probability that the recommended edges materialize in the graph.
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