This paper presents Play.me, a system that exploits social media to generate personalized music playlists. First, we extracted user preferences in music by mining Facebook pro les. Next, given this preliminary playlist based on explicit preferences, we enriched it by adding new artists related to those the user already likes. In this work two di erent enrichment techniques are compared: the rst one relies on knowledge stored on DBpedia while the latter is based on the similarity calculations between semantic descriptions of the artists. A prototype version of the tool was made available online in order to carry out a preliminary user study to evaluate the best enrichment strategy. This paper summarizes the results presented in EC-Web 2012 [3].
According to a recent study3, 31,000 hours of music (and 28 million songs) are currently available on iTunes Store. As a consequence, the problem of information overload is currently felt for online music libraries and multimedia content, as well. However, the recent spread of social networks provides researchers with a rich source to draw to overcome the typical bottleneck represented by user preferences elicitation.
Given this insight, in this work we propose Play.me, a system that leverages
social media for personalizing music playlists. The ltering model is based on
the assumption that information about music preferences can be gathered from
Facebook pro les. Next, explicit Facebook preferences may be enriched with
new artists related to those the user already likes. In this paper we compare
two di erent enrichment techniques: the rst leverages the knowledge stored on
DBpedia while the second is based on similarity calculations between semantics
descriptions of artists. The nal playlist is then ranked and nally presented
3 http://www.digitalmusicnews.com/permalink/2012/120425itunes
to the user that can express her feedback. A prototype version of Play.me was
made available online and a preliminary user study to detect the best
enrichment technique was performed. Generally speaking, this work can be placed in
the area of music recommendation (MR), a topic that has been widely covered in
literature: an early attempt of handling MR problem is due to Shardanand [
The general architecture of Play.me is depicted in Figure 1. The generation
is directly triggered by the user, who invokes the playlist generator module.
The set of her favourite artists is built by mapping her preferences gathered
from her own Facebook pro le (speci cally, by mining the links she posted as
well as the pages she likes) with a set of artists extracted from Last.fm. Given
this preliminary set, the playlist enricher adds new artists by using di erent
enrichment strategies. Finally, for each artist in that set, the most popular tracks
are extracted and the nal playlist is shown to the target user, who can express
her feedback. A working implementation of Play.me has been made available
online (Figure 2). For a complete description of the system it is possible to refer
to [
Enrichment based on Linked Data. The rst technique for enriching user preferences extracted from Facebook relies on the exploitation of DBpedia4. Our approach is based on the assumption that each artist can be mapped to a DBpedia node. The inceptive idea is that the similarity between two artists can be computed according to the number of properties they share (e.g. two Italian bands playing rock music are probably similar). Thus, we decided to use dbpedia-owl:genre (describing the genre played by the artist) and dcterms:subject, that provides information about the musical category. Operationally, we queried a SPARQL endpoint to extract the artists that share as many properties as possible with the target one. Finally, we ranked them according to their playcount in Last.fm. The rst m artists returned by the endpoint are considered as related and added to the set of the favourite artists.
Enrichment based on Distributional Models. Each artist in Play.me is
described through a set of tags (extracted from Last.fm), where each tag provides
information about the genre played by the artist or describes features typical of
her songs (e.g. melanchonic). According to the insight behind distributional
models [
In the experimental evaluation we tried to identify the technique able to generate
the most relevant playlists. We carried out an experiment by involving 30 users
against a Last.fm crawl containing data on 228k artists. In order to identify the
best enrichment technique, we asked users to use the application for three weeks.
In the rst two weeks the system was set with a di erent enrichment technique,
while in the last a simple baseline based on the most popular artists was used.
Given the playlist generated by the system, users were asked to express their
4 http://dbpedia.org
feedback only on the tracks generated by the enrichment process. Results are
reported in Table 1. The parameter m refers to the number of artists added by the
enrichment algorithm for each one extracted from Facebook. It is worth to notice
that both enrichment strategies outperform the baseline. This means that the
social network data actually re ect user preferences. The enrichment technique
that gained the best performance is that based on distributional models. However,
even though this technique gained the best results, a deeper analysis can provide
di erent outcomes. Indeed, with m=3 the gap between the approaches drops
down: this means that a pure content-based representation introduces more noise
than DBpedia, whose e ectiveness stays constant. The good results obtained by
the baseline can be justi ed by the low diversity of the users involved in the
evaluation. More details about the experimental settings are reported in [
In this work we presented Play.me, a system for building music playlists based on social media. Speci cally, we compared two techniques for enriching the playlists, the rst based on DBpedia and the second based on similarity calculations in vector spaces. From the experimental session it emerged that the approach based on distributional models was able to produce the best playlists. Generally speaking, there is still space for future work since the enrichment might be tuned by analyzing di erent DBpedia properties or di erent tags. Furthermore, contextaware personalized playlists could be a promising research direction.