Jacob Clark

Posted on Jun 1, 2019

recomendation algorithms: how can you recommend songs based on a user's past listening history?

#help

I'm interested in getting a user's past listening history from Spotify and being able to suggest songs from the Charts that a user may be interested in listening too.

It's clear that you could use Genre to support this type of algorithm, the more times a user listenes to a genere, it has a higher likelihood of being suggested from the Charts.

I'd like to focus on the activity of a single user and not necessarily other users using the system (content based filtering)

How can this be done in practice? Anybody have any examples of algorithms or tutorials anywhere?

Top comments (5)

stereobooster • Jun 1 '19

I would try to use Graph Database for it. Example

Jane likes song A
Jane likes song B as well
Joe likes song A

So query would look like:

Get all liked songs for Joe, get all users who like the same songs (at least N same songs), get top N songs that those users like and Joe haven't marked as liked.

Main trick here, that this request would kill any relational DB (on join on join...), but GraphDB can handle it just fine.

Jacob Clark • Jun 1 '19

Thanks but this is collaborative filtering and I'm looking for simply a content filtering approach based on a sole user's past activity.

stereobooster • Jun 1 '19 • Edited

It's clear that you could use Genre to support this type of algorithm, the more times a user listenes to a genere, it has a higher likelihood of being suggested from the Charts.

Try Pandora.fm approach. They categorise each song: instruments used, specific vocals or specific guitar rifts etc. Now you have similarity of each song based on N dimensions. As far as I know categorisation is done by humans, not by machines.

UPD quick internet search Automatic Musical Instrument Recognition and Related Topics

Having N dimension you can find similarity of objects with ML algorithm for example with Decision Trees.

EdRome • Jun 1 '19

I suggest using KMeans algorithm to predict the likelihood a user listen to a song. For example, you determine:

song genre
main artist
if songs are composed by a group or solo
date of publication
if it's a remake or not And so on. Once you've got all your variable of interest, encode the ones like names and dates to let the algorithm be trained. When done, take new song characteristics and give them to it, then KMeans will tell you how likely that song would be listen to the user and you should choose the ones with a greater probability If you have a good process capability, then use a post processor algorithm like XGBoost to improve predictions, adding the previous likelihood to your characteristics. I leave here an example to handwriting digit recognition using the algorithms describes above.

Regards!

Jacob Clark • Jun 1 '19

Thanks for this detailed reply! I guess this works well for collaborative filtering but I'm super interested in content based filtering that works off that users activity only.

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recomendation algorithms: how can you recommend songs based on a user's past listening history?

Top comments (5)

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