The author of the text started by thinking about how digital content recommendations work and wondered if they could be applied to their own life. They decided to explore a large music graph for inspiration and thought it would be fun to try to find connections between their musical tastes and those of Neo4j CEO Emil Eifrem, who they had heard was a music lover. The author did some research on social media to learn more about Emil's musical preferences and found a few playlists that gave them a starting point for their graph model. They then loaded data from the MusicBrainz open music encyclopedia into the graph and created relationships between artists, recordings, and tags. After listening to some tracks by Robyn, they discovered a connection between her music and one of their favourite bands, Hercules & Love Affair, through the shared use of certain tags. The author also found connections between Emil's list of favourite artists and their own list, including the artist Loney Dear, who was entirely new to them. They used graph algorithms to compare the tags for both lists and identified some interesting overlaps, but noted that there were still gaps in the data that needed to be addressed before they could build a complete music recommendations engine. Ultimately, the author felt that their experiment had been successful in finding common ground between themselves and Emil through music, and hoped that it would inspire others to explore the power of graph technology for personalization.