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Better genre detection and track recommendation #ML #data #83
For music lovers, discovering new music is essential.
Spotify is well known for the quality of their "Discover Weekly" playlist, containing a personalised selection of tracks based on your listening history.
On Openwhyd, current ways to discover music are:
The first way is purely relying on humans and luck.
The second way relies on a list of 16 genres (a quite limited and vague selection of genres), in which popular tracks are classified, based on the names of the playlists that hold them. This kinda works but it's far from perfect. For example, we had to create a hard-coded rule to prevent Daft Punk songs from being recognised as Punk Rock music!
In order to discover new music by discovering relevant people to follow, we had also experienced showing a measure of profile similarity, but it was only based on the number of artists that were added by both users.
=> Anyone interested in exploring new ways to discover music on Openwhyd?
Examples that could be applied to Openwhyd:
This is a rather exciting feature to add!
Hi @Marinlemaignan !
I'd be happy to replace plTags.js when we have a fully-functional solution that is better than the current one, while maintaining:
One way we could transition gently to a new system:
What do you think?
I have experimented extensively with discog's API.
It's very complete, extremely promising but ... the number of request is of 60 requests .. per minute.
There is no way to go around this. A partnership would be the only solution and I doubt that they would be attracted by a partnership that does not bring them anything.
What we could do is identify albums and point to their products/sellings. They would not split in such a big showcase as openwhyd.
A solution is to host their database. There is docker images to download their monthly dump and index it in mongodb.
But even then a few other problems arise :
A solution that I studied would be scraping ... but they wouldn't like it and what a dirty solution.
I'm not saying it's impossible, just that it's not a bulletproof approach.
Florent Piétot is currently analysing Openwhyd's data set, and thinking of ways to leverage it (e.g. use clustering and/or machine learning techniques for better genre detection and music recommendation).