Fien De Waele Tongeren, Belgium 1H AGO

Sometimes a playlist disappears before you realise how much you depended on it. That happened to a meditation playlist I used constantly for months. Then Spotify removed it, and I discovered an inconvenient detail: I barely knew the track or artist names. I remembered the music and what it did for me, but I could not reliably search for it. The reconstruction began accidentally. While analysing my Spotify listening history for another purpose, AI noticed that I had played roughly the same fifty tracks again and again for about four months. Suddenly, I did not need to remember the missing playlist. My listening history had remembered it for me. A playlist can leave a behavioural fingerprint even when its name and contents are gone. Look for repetitive periods, recurring groups of tracks, similar sequences, and songs that appear together for several months before disappearing around the same time. Step-by-step: 1. I inspected my historical Spotify listening data instead of relying only on which songs I could remember. 2. I used unusual repetition to identify the likely time window. In my case, a four-month cluster helped me recognise the missing meditation playlist, even though I did not know the exact dates beforehand. 3. I built a broad candidate pool of tracks played frequently during that period, recording titles, artists, play counts, dates, listening duration, and nearby tracks. 4. I looked for tracks that repeatedly occurred in the same sessions, on the same days, or in similar sequences. Tracks that travelled together were stronger evidence than songs that were merely popular individually. 5. I compared listening outside the suspected period. A song played throughout several years might be a general favourite, while one that appeared repeatedly within the cluster and rarely elsewhere was a stronger candidate. 6. I used timestamps to recover possible order, including tracks that usually followed each other or appeared near the beginning or end of sessions. Because shuffle may obscure the original order, I treated these smaller sequences as clues rather than proof. 7. I showed the candidate tracks to the person, including names, artists, and, where useful, album art or audio previews. I asked whether each track felt familiar and belonged to the playlist. 8. I relied on recognition rather than recall. Someone may be unable to name a single song but still recognise dozens when presented with them. This is especially useful for instrumental music, mantras, classical works, or unfamiliar names, because remembering the sound does not require remembering its spelling. 9. I ranked candidates as highly likely, likely, possible, or uncertain using repetition, co-occurrence, timing, listening elsewhere, and human confirmation. I did not treat every recovered track as equally certain. 10. I created a provisional playlist from the strongest candidates and listened to it. A list can look convincing in a spreadsheet but feel wrong when played; listening may reveal an unrelated song, a missing transition, or immediate recognition. 11. I used confirmed tracks as anchors and checked which uncertain candidates repeatedly appeared beside them. Data suggested candidates, recognition confirmed anchors, and those anchors improved the next search. 12. I recorded why the playlist mattered, when it was used, and whether its order mattered. Mine was a meditation playlist, so recovering that musical environment mattered even if the exact original list remained uncertain. 13. I saved an external record containing the playlist name, track titles, artists, albums, links or identifiers, reconstruction date, and uncertainty or contextual notes. I kept the listening history, candidate analysis, and confirmed playlist separately so I could revise the result without losing the evidence. The workflow was: analyse listening history → find repeated clusters → locate the time window → collect candidates → compare co-occurrence and sequence → remove unrelated tracks → confirm through recognition → rebuild and listen → refine → save externally. The deeper principle is that humans often remember experiences better than metadata. I remembered that the playlist existed, what it did, and how constantly I played it. I did not remember what most of it was called. The platform had lost the collection, but the listening history still contained its shadow. One observation made it searchable again: > “Hey. For about four months, you were basically listening to the same fifty songs.” 😅🎧