@inproceedings{conf/vl/GoveDSKD11,
  author = {Gove, Robert and Dunne, Cody and Shneiderman, Ben and Klavans, Judith and Dorr, Bonnie J.},
  booktitle = {VL/HCC},
  crossref = {conf/vl/2011},
  editor = {Costagliola, Gennaro and Ko, Andrew Jensen and Cypher, Allen and Nichols, Jeffrey and Scaffidi, Christopher and Kelleher, Caitlin and Myers, Brad A.},
  ee = {http://dx.doi.org/10.1109/VLHCC.2011.6070403},
  interhash = {9820a691a515e4f1a38f31056320c3a2},
  intrahash = {3f86bd37731ba68da7422834d82b55e3},
  isbn = {978-1-4577-1246-3},
  pages = {217-224},
  publisher = {IEEE},
  title = {Evaluating visual and statistical exploration of scientific literature networks.},
  url = {http://dblp.uni-trier.de/db/conf/vl/vlhcc2011.html#GoveDSKD11},
  year = 2011
}

@inproceedings{Zheleva:2010:SMM:1772690.1772794,
  abstract = {User experience in social media involves rich interactions with the media content and other participants in the community. In order to support such communities, it is important to understand the factors that drive the users' engagement. In this paper we show how to define statistical models of different complexity to describe patterns of song listening in an online music community. First, we adapt the LDA model to capture music taste from listening activities across users and identify both the groups of songs associated with the specific taste and the groups of listeners who share the same taste. Second, we define a graphical model that takes into account listening sessions and captures the listening moods of users in the community. Our session model leads to groups of songs and groups of listeners with similar behavior across listening sessions and enables faster inference when compared to the LDA model. Our experiments with the data from an online media site demonstrate that the session model is better in terms of the perplexity compared to two other models: the LDA-based taste model that does not incorporate cross-session information and a baseline model that does not use latent groupings of songs.},
  acmid = {1772794},
  address = {New York, NY, USA},
  author = {Zheleva, Elena and Guiver, John and Mendes Rodrigues, Eduarda and Mili\'{c}-Frayling, Nata\v{s}a},
  booktitle = {Proceedings of the 19th international conference on World wide web},
  doi = {10.1145/1772690.1772794},
  interhash = {7386777403403d0c1b524d1a7cf8065c},
  intrahash = {3a244bf0cd60252269e3c36530e34e8f},
  isbn = {978-1-60558-799-8},
  location = {Raleigh, North Carolina, USA},
  numpages = {10},
  pages = {1019--1028},
  publisher = {ACM},
  series = {WWW '10},
  title = {Statistical models of music-listening sessions in social media},
  url = {http://doi.acm.org/10.1145/1772690.1772794},
  year = 2010
}

@article{carpena:035102,
  author = {Carpena, P. and Bernaola-Galv\'{a}n, P. and Hackenberg, M. and Coronado, A. V. and Oliver, J. L.},
  doi = {10.1103/PhysRevE.79.035102},
  eid = {035102},
  interhash = {3444159872c65ea89d007d1838686acc},
  intrahash = {34dcb1eee3ffa31ff4eb77087343c146},
  journal = {Physical Review E (Statistical, Nonlinear, and Soft Matter Physics)},
  number = 3,
  numpages = {4},
  pages = 035102,
  publisher = {APS},
  title = {Level statistics of words: Finding keywords in literary texts and symbolic sequences},
  url = {http://bioinfo2.ugr.es/TextKeywords/},
  volume = 79,
  year = 2009
}

@book{srl2007,
  editor = {Getoor, Ben Taskar Lise},
  interhash = {1b2e843e978d77deb770183b9b13486d},
  intrahash = {34a3c77de96a47ba99f599123cce0b32},
  title = {Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) },
  year = 2007
}