##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.

What it does

• 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

Bonus

Just go here ;-)

Configure it

edit config.json

Run

 $ node bin/www

Architecture of the app

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.

Known issues

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.