Research has demonstrated that different types of users have different needs when it comes to personalized systems. Factors that have been argued to play a role are user traits and characteristics, such as personality and domain expertise. Within this work we explored the influence of listeners’ active engagement with music, or the extent to which people invest in enjoying music, on the relationship between item popularity and satisfaction with lists of recommendation. Using an online survey built on top of the Spotify API functionality we gathered participants’ most listened tracks and used those to compile playlists containing either popular or non-popular tracks. Our results show that active engagement plays a moderating role on the relationship between popularity and satisfaction, which can more specifically be explained by the extent to which popular songs allow people to discover their musical taste. Where listeners with low active engagement are limited in their discovery by tracks they are familiar with, those with high active engagement can actually use music they are familiar with to further discover their taste. Hence, for low active engagement listeners the most attractive recommendation lists are lists that strike a balance between familiar items and items that enable people to refine their musical taste.