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 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.