Recommender systems have recently been criticized for promoting bias and trapping users into filter bubbles. This phenomenon not only limits potential user interactions but also threatens the broadness of content consumption. In a music recommender, for example, this situation can limit user perspective as music allows people to develop cultural knowledge and empathy. As a fundamental characteristic of users’ content consumption is its diversity, it is necessary to break the bubbles and recommend potentially relevant and diverse songs from outside the influence of such bubbles. To address this problem, we present MRecuri (Music RECommender for filter bUbble diveRsIfication), a music recommendation technique to foster the diversity and novelty of recommendations. A preliminary evaluation over Last.fm listening data showed the potential of MRecuri to increase the diversity and novelty of recommendations compared with state-of-the-art techniques.