@INPROCEEDINGS{XSQZ+13,

 author = {Shuo, Xu and Qingwei, Shi and Xiaodong, Qiao and Lijun, Zhu and Hanmin,

Jung and Seungwoo, Lee and Sung-Pil, Choi},

 title = {Author-Topic over Time ({AToT}): A Dynamic Users' Interest Model},

 booktitle = {Proceedings of the 1st International Workshop on Data-Intensive Intelligence

and Knowledge (DIIK)},

 year = {2013},

 pages = {227--233},

 location = {Jeju, Korea},

 publisher = {Springer},

 abstract = {One of the key problems in upgrading information services towards

knowledge services is to automatically mine latent topics, users'

interests and their evolution patterns from large-scale S&T literatures.

Most of current methods are devoted to either discover static latent

topics and users' interests, or to analyze topic evolution only from

intrafeatures of documents, namely text content without considering

directly extra-features of documents such as authors. To overcome

this problem, a dynamic users’ interest model for documents using

authors and topics with timestamps is proposed, named as Author-Topic

over Time (AToT) model, and collapsed Gibbs sampling method is utilized

for inferring model parameters. This model is not only able to discover

latent topics and users' interests, but also to mine their changing

patterns over time. Finally, the extensive experimental results on

NIPS dataset with 1,740 papers indicate that our AToT model is feasible

and efficient.},

 keywords = {Author-Topic (AT) Model sep Topic over Time (ToT) Model sep Author-Topic

over Time (AToT) Model sep Dynamic UUser' Interest Model sep Collapsed

Gibbs Sampling}

}

Fulltext：AToT.pdf

slides：AToT (slides).pdf