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

%0 = inproceedings %A = Balasubramanyan, Ramnath and Dalvi, Bhavana Bharat and Cohen, William W. %B = ECML/PKDD (2) %D = 2013 %I = Springer %T = From Topic Models to Semi-supervised Learning: Biasing Mixed-Membership Models to Exploit Topic-Indicative Features in Entity Clustering. %U = http://dblp.uni-trier.de/db/conf/pkdd/pkdd2013-2.html#BalasubramanyanDC13

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.

@inproceedings{Zheleva:2010:SMM:1772690.1772794, author = {Zheleva, Elena and Guiver, John and Mendes Rodrigues, Eduarda and Milić-Frayling, Nataša}, title = {Statistical models of music-listening sessions in social media}, booktitle = {Proceedings of the 19th international conference on World wide web}, series = {WWW '10}, publisher = {ACM}, address = {New York, NY, USA}, year = {2010}, pages = {1019--1028}, url = {http://doi.acm.org/10.1145/1772690.1772794}, doi = {10.1145/1772690.1772794}, isbn = {978-1-60558-799-8}, keywords = {models, music, statistical, toread}, 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.} }

%0 = inproceedings %A = Zheleva, Elena and Guiver, John and Mendes Rodrigues, Eduarda and Milić-Frayling, Nataša %B = Proceedings of the 19th international conference on World wide web %C = New York, NY, USA %D = 2010 %I = ACM %T = Statistical models of music-listening sessions in social media %U = http://doi.acm.org/10.1145/1772690.1772794

@inproceedings{journals/jmlr/ChangB09, author = {Chang, Jonathan and Blei, David M.}, title = {Relational Topic Models for Document Networks.}, editor = {Dyk, David A. Van and Welling, Max}, booktitle = {AISTATS}, series = {JMLR Proceedings}, publisher = {JMLR.org}, year = {2009}, volume = {5}, pages = {81-88}, url = {http://dblp.uni-trier.de/db/journals/jmlr/jmlrp5.html#ChangB09}, keywords = {models, sota, topic, toread} }

%0 = inproceedings %A = Chang, Jonathan and Blei, David M. %B = AISTATS %D = 2009 %I = JMLR.org %T = Relational Topic Models for Document Networks. %U = http://dblp.uni-trier.de/db/journals/jmlr/jmlrp5.html#ChangB09

Networks are ubiquitous in science and have become a focal point for
scussion in everyday life. Formal statistical models for the analysis of
twork data have emerged as a major topic of interest in diverse areas of
udy, and most of these involve a form of graphical representation.
obability models on graphs date back to 1959. Along with empirical studies in
cial psychology and sociology from the 1960s, these early works generated an
tive network community and a substantial literature in the 1970s. This effort
ved into the statistical literature in the late 1970s and 1980s, and the past
cade has seen a burgeoning network literature in statistical physics and
mputer science. The growth of the World Wide Web and the emergence of online
tworking communities such as Facebook, MySpace, and LinkedIn, and a host of
re specialized professional network communities has intensified interest in
e study of networks and network data. Our goal in this review is to provide
e reader with an entry point to this burgeoning literature. We begin with an
erview of the historical development of statistical network modeling and then
introduce a number of examples that have been studied in the network
terature. Our subsequent discussion focuses on a number of prominent static
d dynamic network models and their interconnections. We emphasize formal
del descriptions, and pay special attention to the interpretation of
rameters and their estimation. We end with a description of some open
oblems and challenges for machine learning and statistics.

@misc{goldenberg2009survey, author = {Goldenberg, Anna and Zheng, Alice X and Fienberg, Stephen E and Airoldi, Edoardo M}, title = {A survey of statistical network models}, year = {2009}, note = {cite arxiv:0912.5410Comment: 96 pages, 14 figures, 333 references}, url = {http://arxiv.org/abs/0912.5410}, keywords = {models, network, sota, survey, topic, toread}, abstract = {Networks are ubiquitous in science and have become a focal point fordiscussion in everyday life. Formal statistical models for the analysis ofnetwork data have emerged as a major topic of interest in diverse areas ofstudy, and most of these involve a form of graphical representation.Probability models on graphs date back to 1959. Along with empirical studies insocial psychology and sociology from the 1960s, these early works generated anactive network community and a substantial literature in the 1970s. This effortmoved into the statistical literature in the late 1970s and 1980s, and the pastdecade has seen a burgeoning network literature in statistical physics andcomputer science. The growth of the World Wide Web and the emergence of onlinenetworking communities such as Facebook, MySpace, and LinkedIn, and a host ofmore specialized professional network communities has intensified interest inthe study of networks and network data. Our goal in this review is to providethe reader with an entry point to this burgeoning literature. We begin with anoverview of the historical development of statistical network modeling and thenwe introduce a number of examples that have been studied in the networkliterature. Our subsequent discussion focuses on a number of prominent staticand dynamic network models and their interconnections. We emphasize formalmodel descriptions, and pay special attention to the interpretation ofparameters and their estimation. We end with a description of some openproblems and challenges for machine learning and statistics.} }

%0 = misc %A = Goldenberg, Anna and Zheng, Alice X and Fienberg, Stephen E and Airoldi, Edoardo M %B = } %C = %D = 2009 %I = %T = A survey of statistical network models} %U = http://arxiv.org/abs/0912.5410

@inproceedings{conf/ai/ShafieiM08, author = {Shafiei, M. Mahdi and Milios, Evangelos E.}, title = {A Statistical Model for Topic Segmentation and Clustering.}, editor = {Bergler, Sabine}, booktitle = {Canadian Conference on AI}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, year = {2008}, volume = {5032}, pages = {283-295}, url = {http://dblp.uni-trier.de/db/conf/ai/ai2008.html#ShafieiM08}, isbn = {978-3-540-68821-1}, keywords = {models, segment, sota, topic} }

%0 = inproceedings %A = Shafiei, M. Mahdi and Milios, Evangelos E. %B = Canadian Conference on AI %D = 2008 %I = Springer %T = A Statistical Model for Topic Segmentation and Clustering. %U = http://dblp.uni-trier.de/db/conf/ai/ai2008.html#ShafieiM08

@incollection{bleigjt03, author = {Blei, D. M. and Griffiths, T. L. and Jordan, M. I. and Tenenbaum, J. B.}, title = {Hierarchical topic models and the nested Chinese restaurant process}, booktitle = {Advances in Neural Information Processing Systems 16}, publisher = {MIT Press}, address = {Cambridge, MA}, year = {2004}, keywords = {hierarchical, models, sota, topic} }

%0 = incollection %A = Blei, D. M. and Griffiths, T. L. and Jordan, M. I. and Tenenbaum, J. B. %B = Advances in Neural Information Processing Systems 16 %C = Cambridge, MA %D = 2004 %I = MIT Press %T = Hierarchical topic models and the nested Chinese restaurant process

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

%0 = article %A = Hofmann, Thomas %C = New York, NY, USA %D = 2004 %I = ACM Press %T = Latent semantic models for collaborative filtering