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Personalized Recommendations on a Matching Platform

I study a platform-mediated two-sided matching with incomplete information. Platform users voluntarily disclose their taste information to the platform, and the platform aggregates and redistributes the collected information back to each user before they form matches. I characterize conditions under which the platform can (or cannot) achieve an optimal matching outcome by the exchange of information. It is shown that a simple rule of recommendation can induce full disclosure of user information and an optimal matching under a certain condition. I also study an optimal customized pricing schedule and show that full extraction of user surplus is possible. Lastly, as an application, I study a two-way communication protocol by cheap-talk and demonstrate that communication strictly improves eﬃciency in any circumstances.