Author-Topic over Time (AToT): A Dynamic Users' Interest Model

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 intra-features 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.

[1]  L. R. Shenton,et al.  Parameter estimation for the beta distribution , 1992 .

[2]  Thomas L. Griffiths,et al.  Probabilistic author-topic models for information discovery , 2004, KDD.

[3]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Andrew McCallum,et al.  Group and Topic Discovery from Relations and Their Attributes , 2005, NIPS.

[5]  Andrew McCallum,et al.  Topics over time: a non-Markov continuous-time model of topical trends , 2006, KDD '06.

[6]  John D. Lafferty,et al.  Dynamic topic models , 2006, ICML.

[7]  Feng Qiu,et al.  Automatic identification of user interest for personalized search , 2006, WWW '06.

[8]  Andrew McCallum,et al.  Expertise modeling for matching papers with reviewers , 2007, KDD '07.

[9]  Andrew McCallum,et al.  Topic and Role Discovery in Social Networks with Experiments on Enron and Academic Email , 2007, J. Artif. Intell. Res..

[10]  Chong Wang,et al.  Continuous Time Dynamic Topic Models , 2008, UAI.

[11]  Claire Elayne Bangerter Owen.  Parameter Estimation for the Beta Distribution , 2008 .

[12]  Ruoming Jin,et al.  Topic level expertise search over heterogeneous networks , 2010, Machine Learning.

[13]  Thomas L. Griffiths,et al.  Learning author-topic models from text corpora , 2010, TOIS.

[14]  Noriaki Kawamae,et al.  Author interest topic model , 2010, SIGIR.

[15]  Noriaki Kawamae.  Latent interest-topic model: finding the causal relationships behind dyadic data , 2010, CIKM '10.

[16]  Jinhyung Kim,et al.  User-centered innovative technology analysis and prediction application in mobile environment , 2013, Multimedia Tools and Applications.