Music recommender systems are utilised by many music streaming platforms to provide new artist and song recommendations on a personalised basis to listeners. By applying dynamic data modelling techniques, music recommender systems add to the current ecosystem of music recommendations. This includes direct and indirect, such as word of mouth, musical journalism, TV, radio, and live events. We report results from a study designed to investigate listener receptivity to music recommender systems, compared to editorial and peer based recommendations. Our results suggest participants’ self-reported receptivity is significantly greater for music recommender systems compared to editorial and peer based. However, results from participants’ evaluation of playlists perceived to be created by each recommender source suggests a significantly greater duration of play for peer-based recommendation playlists. No difference was found in likelihood to spend time or money on artists when only the recommendation source was considered. We discuss these results in relation to how anchoring bias may influence listeners’ behaviours and how platform design may be informed based on their requirements and objectives.