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

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

1. song genre
2. main artist
3. if songs are composed by a group or solo
4. date of publication
5. 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!