Collaborative filtering recommenders leverage past user-item ratings in order to predict ratings for new items. One of the most critical steps in such methods corresponds to the formation of the neighbourhood that contains the most similar users or items, so that the ratings associated with them can be employed for predicting new ratings. This work proposes to perform the combination of content-based and ratings-based evidence during the neighbourhood formation step and thus identify the most similar neighbours in a hybrid manner. To this end, DBSCAN, a density-based clustering approach, is applied for identifying the most similar users or items by considering the ratings-based and the content-based similarities, both individually and in combination. The resulting hybrid cluster-based CF recommendation scheme is then evaluated on the latest small MovieLens100k dataset and the experimental results indicate the potential of the proposed approach.