Constrained Coupled Matrix-Tensor Factorization and its Application in Pattern and Topic Detection.

Traditionally, time-evolving topic discovery approaches have focused on the temporal evolution of the topic itself. However, especially in settings where content is contributed by a community or a crowd, an orthogonal notion of time is the one that pertains to the level of expertise of the content creator: the more experienced the creator, the more advanced the topic will be. In this paper, we propose a novel time-evolving topic discovery method which, in addition to the extracted topics, is able to identify the evolution of that topic over time, as well as the level of difficulty of that topic, as it is inferred by the level of expertise of its main contributors. Our method is based on a novel formulation of Constrained Coupled Matrix-Tensor Factorization, which adopts constraints that are well motivated for, and, as we demonstrate, are necessary for high-quality topic discovery.