@inproceedings{conf/ai/ShafieiM08,
  author = {Shafiei, M. Mahdi and Milios, Evangelos E.},
  booktitle = {Canadian Conference on AI},
  crossref = {conf/ai/2008},
  editor = {Bergler, Sabine},
  ee = {http://dx.doi.org/10.1007/978-3-540-68825-9_27},
  interhash = {1ed1fddf0ac4762ea8debac2ee80b936},
  intrahash = {80e27cd4ea288b0ab6bcc1c67841364e},
  isbn = {978-3-540-68821-1},
  pages = {283-295},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {A Statistical Model for Topic Segmentation and Clustering.},
  url = {http://dblp.uni-trier.de/db/conf/ai/ai2008.html#ShafieiM08},
  volume = 5032,
  year = 2008
}

@inproceedings{conf/pkdd/BalasubramanyanDC13,
  author = {Balasubramanyan, Ramnath and Dalvi, Bhavana Bharat and Cohen, William W.},
  booktitle = {ECML/PKDD (2)},
  crossref = {conf/pkdd/2013-2},
  editor = {Blockeel, Hendrik and Kersting, Kristian and Nijssen, Siegfried and Zelezný, Filip},
  ee = {http://dx.doi.org/10.1007/978-3-642-40991-2_40},
  interhash = {9a32b7cc059a500ea302d0aa65036682},
  intrahash = {e56623d21a1b7bcb442cd15fe098bb70},
  isbn = {978-3-642-40990-5},
  pages = {628-642},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {From Topic Models to Semi-supervised Learning: Biasing Mixed-Membership Models to Exploit Topic-Indicative Features in Entity Clustering.},
  url = {http://dblp.uni-trier.de/db/conf/pkdd/pkdd2013-2.html#BalasubramanyanDC13},
  volume = 8189,
  year = 2013
}

@incollection{bleigjt03,
  address = {Cambridge, MA},
  author = {Blei, D. M. and Griffiths, T. L. and Jordan, M. I. and Tenenbaum, J. B.},
  booktitle = {Advances in {N}eural {I}nformation {P}rocessing {S}ystems 16},
  interhash = {f185b4657e25c733ee613bece516b3c5},
  intrahash = {3e438204424fa2c6e8915bd8f0baf112},
  publisher = {MIT Press},
  title = {Hierarchical topic models and the nested {C}hinese restaurant process},
  year = 2004
}

@misc{goldenberg2009survey,
  abstract = {Networks are ubiquitous in science and have become a focal point for
discussion in everyday life. Formal statistical models for the analysis of
network data have emerged as a major topic of interest in diverse areas of
study, and most of these involve a form of graphical representation.
Probability models on graphs date back to 1959. Along with empirical studies in
social psychology and sociology from the 1960s, these early works generated an
active network community and a substantial literature in the 1970s. This effort
moved into the statistical literature in the late 1970s and 1980s, and the past
decade has seen a burgeoning network literature in statistical physics and
computer science. The growth of the World Wide Web and the emergence of online
networking communities such as Facebook, MySpace, and LinkedIn, and a host of
more specialized professional network communities has intensified interest in
the study of networks and network data. Our goal in this review is to provide
the reader with an entry point to this burgeoning literature. We begin with an
overview of the historical development of statistical network modeling and then
we introduce a number of examples that have been studied in the network
literature. Our subsequent discussion focuses on a number of prominent static
and dynamic network models and their interconnections. We emphasize formal
model descriptions, and pay special attention to the interpretation of
parameters and their estimation. We end with a description of some open
problems and challenges for machine learning and statistics.},
  author = {Goldenberg, Anna and Zheng, Alice X and Fienberg, Stephen E and Airoldi, Edoardo M},
  interhash = {bab22de06306d84cf357aadf48982d87},
  intrahash = {5e341981218d7cd89416c3371d56c794},
  note = {cite arxiv:0912.5410Comment: 96 pages, 14 figures, 333 references},
  title = {A survey of statistical network models},
  url = {http://arxiv.org/abs/0912.5410},
  year = 2009
}

@inproceedings{journals/jmlr/ChangB09,
  author = {Chang, Jonathan and Blei, David M.},
  booktitle = {AISTATS},
  crossref = {conf/aistats/2009},
  editor = {Dyk, David A. Van and Welling, Max},
  ee = {http://www.jmlr.org/proceedings/papers/v5/chang09a.html},
  interhash = {f3431fd69b315a22422a2c0f15ee0b71},
  intrahash = {86f665b74ecabb56e81542e0f052a331},
  pages = {81-88},
  publisher = {JMLR.org},
  series = {JMLR Proceedings},
  title = {Relational Topic Models for Document Networks.},
  url = {http://dblp.uni-trier.de/db/journals/jmlr/jmlrp5.html#ChangB09},
  volume = 5,
  year = 2009
}

@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{963774,
  address = {New York, NY, USA},
  author = {Hofmann, Thomas},
  doi = {http://doi.acm.org/10.1145/963770.963774},
  interhash = {ffd4c7560d25f5c0b6f92dbba0bbfc79},
  intrahash = {a887c9d3b1d49ae260a7b3cd1118d36a},
  issn = {1046-8188},
  journal = {ACM Trans. Inf. Syst.},
  number = 1,
  pages = {89--115},
  publisher = {ACM Press},
  title = {Latent semantic models for collaborative filtering},
  volume = 22,
  year = 2004
}