Introduction : Last.fm recommendations
The SoundSuggest application uses Last.fm data and tries to establish a context for recommendations based on the active user’s neighbourhood and the active user’s top rated items. Although I could not find an official source for the Last.fm recommender system’s algorithm, it is assumed that it is based on some variant of collaborative filtering [1, 2].
The objective of collaborative filtering is to find a sort of community, i.e., the neighbourhood of the active user, based on similarities between these profiles . It should come as no surprise that certain items will be strongly linked due to geographic similarities between profiles, as users living in similar places are likely to have heard of local artists, while people living in other countries won’t. Belgium has a relatively small music scene compared to the United States or the United Kingdom, as a result Belgian artists seem to cluster together based on the geographic aspects, rather than musical similarities. This becomes apparent when looking at the similar artists for certain Belgian musicians as can be seen on Figure 1.
From a content-based perspective, collaborative filtering produces more serendipitous suggestions. Still, one could argue that user location introduces a bias into the system. Based on style of music, a band such as Goose is still quite different from a band such as Das Pop. So it is odd that the similar artists for Das Pop are in fact all Belgian – see Figure 2, while there would probably be many other artists that fit their style of music much better that are not necessarily Belgian.
Whether or not this serendipity improves or decreases the quality of recommendations is of course up to the end user. Nonetheless, it indicates some of the limits, as well as the strengths of collaborative filtering, and for the Last.fm algorithm in particular.
Implications for the SoundSuggest application
When creating the data structure used in the SoundSuggest application, as explained here, there is a mismatch between the neigbourhood and some of the recommendations. If the profile contains both well-known artists, as well as artists that are strongly geographically linked, it may occur that the neighbourhood is largely based on the well-known artists. As a result, it is very likely that the connectivity between neighbours and this second group of artists will be very low to non-existent. This phenomenon can be seen in Figure 3, where in fact all the Belgian bands aren’t in any of the neighbouring profiles.
A user of the application may derive that these suggestions are poor in quality. In my opinion, often is this the case. I’m much more interested in artists that are similar to bands I listen to a lot, rather than just local bands just because they happen to be local. To solve this, one option would be to discard the artists with low connectivity in the graph, and search for additional recommendations that hopefully provide better connectivity.
In further developing the application, it would probably be interesting to find out what the impact is of non-connected artists on the user experience of the visualization.
 Wikipedia, Last.fm – Wikipedia, the free encyclopedia, 21 March 2013, [Online] Available at: http://en.wikipedia.org/wiki/Last.fm#Recommendations [Accessed on 7 April 2013]
 Last.fm, 2013, [Online] Available at: http://www.last.fm/api [Accessed on 7 April 2013]
 Rajaraman A., Leskovec J. and Ullman J. D., 2012, Mining Massive Datasets