LastFM_dataset

OpenML dataset with id 44199

Author name not available (Why is that?)

Full work available at URL: https://api.openml.org/data/v1/download/22103414/LastFM_dataset.arff

Upload date: 8 September 2022

Dataset Characteristics

Number of features: 4 (numeric: 1, symbolic: 0 and in total binary: 0 )
Number of instances: 289,955
Number of instances with missing values: 0
Number of missing values: 0

Description

Last FM Dataset

Version

Version 1.0 (May 2011)

Description

   This dataset contains social networking, tagging, and music artist listening information 
   from a set of 2K users from Last.fm online music system.
   http://www.last.fm

   The dataset is released in the framework of the 2nd International Workshop on 
   Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) 
   http://ir.ii.uam.es/hetrec2011 
   at the 5th ACM Conference on Recommender Systems (RecSys 2011)
   http://recsys.acm.org/2011

License

  The users' names and other personal information in Last.fm are not provided in the dataset.

  The data contained in hetrec2011-lastfm-2k.zip is made available for non-commercial use.
  
  Those interested in using the data in a commercial context should contact Last.fm staff:
  http://www.lastfm.com/about/contact

Acknowledgements

  This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02), 
  and the Regional Government of Madrid (S2009TIC-1542).

References

  When using this dataset you should cite:
     - Last.fm website, http://www.lastfm.com

  You may also cite HetRec'11 workshop as follows:

  @inproceedings{Cantador:RecSys2011,
     author = {Cantador, Iv\'{a}n and Brusilovsky, Peter and Kuflik, Tsvi},
     title = {2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011)},
     booktitle = {Proceedings of the 5th ACM conference on Recommender systems},
     series = {RecSys 2011},
     year = {2011},
     location = {Chicago, IL, USA},
     publisher = {ACM},
     address = {New York, NY, USA},
     keywords = {information heterogeneity, information integration, recommender systems},
  }

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