"The LFM-1b Dataset for Music Retrieval and Recommendation"
: Proceedings of the ACM International Conference on Multimedia Retrieval (ICMR 2016), 6-2016
The LFM-1b Dataset for Music Retrieval and Recommendation
Sprache des Titels:
Proceedings of the ACM International Conference on Multimedia Retrieval (ICMR 2016)
We present the LFM-1b dataset of more than one billion
music listening events created by more than 120
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
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
size and (ii) a wide range of additional user descriptors tha
reflect their music taste and consumption behavior.