Most research in recommender systems is focused on the problem of identifying and ranking items that are relevant for the individual users but unknown to them. The potential value of such systems is to help users discover new items, e.g., in e-commerce settings. Many real-world systems however also utilize recommendation lists for a different goal, namely to remind users of items that they have viewed or consumed in the past. In this work, we aim to quantify the value of such reminders in recommendation lists ("recominders"), which has to our knowledge not been done in the past. We first report the results of a live experiment in which we applied a naive reminding strategy on an online platform and compare them with results obtained through different offline analyses. We then propose more elaborate reminding techniques, which aim to avoid reminders of too obvious or of already outdated items. Overall, our results show that although reminders do not lead to new item discoveries, they can be valuable both for users and service providers.