Markus Schedl,
"The LFM-1b Dataset for Music Retrieval and Recommendation"
: Proceedings of the ACM International Conference on Multimedia Retrieval (ICMR 2016), 6-2016
Original Titel:
The LFM-1b Dataset for Music Retrieval and Recommendation
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the ACM International Conference on Multimedia Retrieval (ICMR 2016)
Original Kurzfassung:
We present the LFM-1b dataset of more than one billion
music listening events created by more than 120
,
000 users
of Last.fm. Each listening event is characterized by artist
,
album, and track name, and further includes a timestamp.
On the (anonymous) user level, basic demographics and a
selection of more elaborate user descriptors are included.
The dataset is foremost intended for benchmarking in mu-
sic information retrieval and recommendation. To facili-
tate experimentation in a straightforward manner, it also
includes a precomputed user-item-playcount matrix. In ad-
dition, sample Python scripts showing how to load the data
and perform efficient computations are provided. An imple-
mentation of a simple collaborative filtering recommender
rounds off the code package.
We discuss in detail the LFM-1b dataset?s acquisition,
availability, statistics, and content, and place it in the c
on-
text of existing datasets. We also showcase its usage in a
simple artist recommendation task, whose results are in-
tended to serve as baseline against which more elaborate
techniques can be assessed. The two unique features of the
dataset in comparison to existing ones are (i) its substantia
l
size and (ii) a wide range of additional user descriptors tha
t
reflect their music taste and consumption behavior.
Forschungs-