I’m currently developing an application that needs to cross-reference music tracks from Spotify with songs in an external music database. The matching process has been challenging, particularly when dealing with variations like “featuring”, “ft.”, remix indicators, and other modifiers in song titles.
I’m wondering if anyone has comprehensive knowledge about how Spotify formats track names when they’re retrieved through their Web API? Are there documented standards for how they handle collaborations, remixes, live versions, and other track variations?
Additionally, I’d appreciate insights into artist name formatting conventions used by the platform. While these seem more consistent than track names, I want to ensure my matching algorithm accounts for all possible variations.
Has anyone encountered similar challenges or found reliable documentation about these naming patterns? Any guidance would be extremely helpful for improving my matching accuracy.
spotify’s data is a mess. they don’t follow any standards - the same song gets formatted completely differently depending on who uploaded it. I’ve had good luck using regex patterns to clean things up before comparing tracks. strip out everything after ‘feat’ or ‘remix’, convert to lowercase, and remove special characters first.
I ran into this exact issue building a music recommendation system last year. Spotify’s naming is all over the place - it’s a nightmare for matching. Track titles are wildly inconsistent depending on who uploaded them and when. You’ll see “feat.”, “featuring”, “ft.”, “with”, and “&” all used for the same collab. Remixes are just as bad - “(Remix)”, “- Remix”, “[Remix]”, or sometimes just tacked on with no separator. Artist names are better but still messy with encoding issues (especially non-Latin characters) and weird spacing. I built a preprocessing pipeline that normalizes the common variations before matching - strips brackets/parentheses, standardizes collab keywords, removes suffixes. Definitely use fuzzy string matching with something like fuzzywuzzy since exact matches fail constantly even after cleaning. Spotify’s API docs don’t give formatting guidelines, so you’ve got to handle all this programmatically.
Been dealing with this headache for years across multiple music projects. The inconsistency is brutal and gets worse when you’re pulling from multiple sources.
What saved me was an automated pipeline that handles all the preprocessing and matching. Instead of manually writing regex patterns and fuzzy matching rules, I built a workflow in Latenode that does the heavy lifting.
The workflow pulls tracks from Spotify API, runs them through multiple normalization steps (removes brackets, standardizes featuring formats, cleans artist names), then uses fuzzy matching to compare against external databases. Best part? I can add new cleaning rules or adjust matching thresholds without touching code.
I also added automatic retry logic for failed matches and logging so I can see which patterns cause the most problems. This lets me continuously improve matching accuracy.
The whole system runs automatically when new tracks get added, and I monitor everything from a simple dashboard. Way better than maintaining preprocessing scripts that break every time Spotify changes something.