Scalable Dynamic Nonparametric Bayesian Models of Content and Users
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 3111-3114.
Abstract
Online content have become an important medium to disseminate information and express opinions. With their proliferation, users are faced with the problem of missing the big picture in a sea of irrelevant and/or diverse content. In this paper, we addresses the problem of information organization of online document collections, and provide algorithms that create a structured representation of the otherwise unstructured content. We leverage the expressiveness of latent probabilistic models (e.g., topic models) and non-parametric Bayes techniques (e.g., Dirichlet processes), and give online and distributed inference algorithms that scale to terabyte datasets and adapt the inferred representation with the arrival of new documents. This paper is an extended abstract of the 2012 ACM SIGKDD best doctoral dissertation award of Ahmed [2011].
@inproceedings{ijcai2013p467,
title = {Scalable Dynamic Nonparametric Bayesian Models of Content and Users},
author = {Ahmed, Amr and Xing, Eric P.},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {3111--3114},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/467},
}
TY - CPAPER TI - Scalable Dynamic Nonparametric Bayesian Models of Content and Users AU - Ahmed, Amr AU - Xing, Eric P. T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 3111 EP - 3114 UR - https://ijcai.org/proceedings/2013/467 ER -Copied
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