##Introduction
This is an example of the usage of the last.fm api. It was created in 4 days for a work sample for a job interview.
- Get the current top Artists
- Get their tags associated
- Clean the tags that are too few in number and format it better (trim, lower it)
- Compute the probability of associations
- For artists use Pearson coefficient
- For tags use cosine with tfidf weight
- Store everything in a mongodb database
Just go here ;-)
edit config.json
$ node bin/www
lastfmPuller.js is the main file. It does all the collection and calculation of similarity. The rest is kinda the web part with a typical express architecture.
According to last.fm forum and my observations, the playcount and listeners in chart.getTopArtists are for the past 6 months.
Therefore, the ranking is actually based on the number of listeners for the past 6 months which express more a trend than an overall ranking.
i.e. There might be this new artist which is really hype and have been listenned a lot in the past 6 month. He might appear first in the query chart.getTopArtists but overall he is far from the top Artists.
In order to be consistant in my metric, I populate listeners and playcount from artist.getInfo which are the overall informations.
However, this is extremely costy as I have to do an additionnal query for each artist...
An alternative solution should be found if we want to scale it.