The primary contribution of this paper resides in devising constant-factor approximation guarantees for revenue maximization in two-sided matching markets with general pairwise rewards. A major distinction between our work and state-of-the-art results in this context (Ashlagi et al., 2022; Torrico et al., 2023) is that, for the first time, we are able to address reward maximization, reflected by assigning each customer-supplier pair an arbitrarily-valued reward. Our performance guarantees apply to two different settings: the customized model and the inclusive model. The fundamental difference between these settings lies in whether the platform should display to each supplier all selecting customers, as in the inclusive model, or whether the platform can further personalize this set, as in the customized model. Technically speaking, our algorithmic approaches and their analysis revolve around presenting a closely related combinatorial problem, a novel linear relaxation, and developing tailor-made analytical ideas. In both models considered, these ingredients allow us to overcome the lack of submodularity and subadditivity that stems from pairwise rewards, plaguing the applicability of existing methods. We complement our theoretical results with an extensive computational study under different classes of instances where we analyze the empirical reward and running time performance of our proposed methods.

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