The Lfm-1b Dataset For Music Retrieval And Recommendation

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 music information retrieval and recommendation. To facilitate experimentation in a straightforward manner, it also includes a precomputed user-item-playcount matrix. In addition, sample Python scripts showing how to load the data and perform efficient computations are provided. An implementation 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 context of existing datasets. We also showcase its usage in a simple artist recommendation task, whose results are intended 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 substantial size and (ii) a wide range of additional user descriptors that reflect their music taste and consumption behavior.