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Thick two-sided matching platforms, such as the room-rental market, face the challenge of showing relevant objects to users to reduce search costs. Many platforms use ranking algorithms to determine the order in which alternatives are shown to users. Ranking algorithms may depend on simple criteria, such as how long a listing has been on the platform, or incorporate more sophisticated aspects, such as personalized inferences about users' preferences. Using rich data on a room rental platform, we show how ranking algorithms can be a source of unnecessary congestion, especially when the ranking is invariant across users. Invariant rankings induce users to view, click, and request the same rooms in the platform we study, greatly limiting the number of matches it creates. We estimate preferences and simulate counterfactuals under different ranking algorithms varying the degree of user personalization and variation across users. In our case, increased personalization raises both user match utility and congestion, which leads to a trade-off. We find that the current outcome is inefficient as it lies below the possibility frontier, and propose alternatives that improve upon it.