The matching setting, a particular case of recommendation problem, focuses on applications where a so-called item can be attributed to at most one user, with the job market and the matrimonial market as chief examples. In such cases, recommending the items preferred by each user might contribute to a congestion issue as users aiming at the same item cannot be all satisfied. While some state of art approaches proceed by repairing the recommendation policy to account for the congestion issue, other approaches take inspiration from the optimal transport (OT) framework, and aim to map the user population onto the item population in order to minimize some global transportation cost. In OT-based recommendation state-of-art approaches, the collaborative matrix (reporting the user-item matches) is interpreted as if it were the result of an OT plan; the underlying transport cost is inferred and used e.g. to propose new congestion-avoiding recommendation policies. In this paper, another OT-based recommendation strategy is defined, noting that the collaborative matrix reflecting the individual decisions can hardly be considered as the result of a (centralized) OT plan. Accordingly, the proposed algorithm first learns a recommendation score from the data and then defines a cost matrix, with the transportation cost decreasing depending on the matching relevance. The optimal transport plan is thereafter used for the recommendation. The experimental validation of the approach presents comparative results on proprietary real-world data on the job market, illustrating the trade-off between the recall and the congestion indicators.